Primary Headwater Stream Analyses
Nathan Byer
2023-06-01
Reading in data
phwh<-read.csv("PHWH_postgresexport.csv",header=T,na.strings=c("","NA"))creating summary files for downstream analysis
sitechecks<-data.frame(phwh$stream_name,phwh$date)
colnames(sitechecks)<-c("stream_names","date")
sitechecks$date<- as.Date(sitechecks$date , format = "%Y/%m/%d")
sitechecks$datecount<-as.numeric(sitechecks$date-min(sitechecks$date)+1)
sitechecks_surveyhistory<-sitechecks %>%
group_by(stream_names) %>%
mutate(check=as.character(dense_rank(date))) %>%
arrange(stream_names) %>%
distinct(stream_names, datecount, .keep_all = TRUE) %>%
spread(check,datecount) %>%
summarise(`1` = mean(`1`,na.rm=T),
`2`=mean(`2`,na.rm=T),
`3`=mean(`3`,na.rm=T),
`4`=mean(`4`,na.rm=T),
`5`=mean(`5`,na.rm=T),
`6`=mean(`6`,na.rm=T),
`7`=mean(`7`,na.rm=T),
`8`=mean(`8`,na.rm=T),
`9` = mean(`9`,na.rm=T),
`10`=mean(`10`,na.rm=T),
`11`=mean(`11`,na.rm=T),
`12`=mean(`12`,na.rm=T),
`13`=mean(`13`,na.rm=T),
`14`=mean(`14`,na.rm=T),
`15`=mean(`15`,na.rm=T),
`16`=mean(`16`,na.rm=T))
phwh_subset<-phwh %>%
dplyr::select(stream_name,reservation,river_basin,latitude,longitude,date,substrate_metric_total,percent_best_types,per_boulder_slabs,per_boulder_256mm,per_bedrock,per_cobble,per_gravel,per_sand,per_silt,per_leaf_woody,per_detritus,per_clay_hardpan,per_muck,per_artificial,pool_metric_total,pool_depth_.cm.,bankfull_metric_total,average_bankfull_.m.,sinuosity,base_flow,date_last_rain,amt_last_rain,X._canopy_open,temp,dissolved_oxygen_mgl,ph,conductivity,fish_observed,fish_time_collecting_minutes,frogs_tadpoles_observed,salamanders_observed,salamander_time_collecting_minutes,aquatic_macros_observed,macro_time_collecting_minutes,mountain_dusky_larvae,mountain_dusky_juveniles,mountain_dusky_adults,northern_dusky_larvae,northern_dusky_juveniles,northern_dusky_adults,two_lined_larvae,two_lined_juveniles,two_lined_adults,long_tailed_larvae,long_tailed_juveniles,long_tailed_adults,northern_red_larvae,northern_red_juveniles,northern_red_adults,mole_larvae,mole_juveniles,mole_adults,four_toed_larvae,four_toed_juveniles,four_toed_adults,red_backed_juveniles,red_backed_adults,slimy_juveniles,slimy_adults,blacknose_dace,bluegill_sunfish,bluntnose_minnow,brindled_madtom,brook_stickleback,brook_trout,central_mudminnow,central_stoneroller_minnow,common_shiner,creek_chub,eastern_sand_darter,fantail_darter,fathead_minnow,golden_shiner,goldfish,grass_pickerel,greenside_darter,green_sunfish,johnny_darter,largemouth_bass,longear_sunfish,longnose_dace,mottled_sculpin,northern_hog_sucker,pumpkinseed_sunfish,rainbow_darter,rainbow_trout,redbelly_dace,redside_dace,river_chub,silverjaw_minnow,smallmouth_bass,spotfin_shiner,stonecat_madtom,striped_shiner,trout_perch,warmouth_sunfish,white_sucker,yellow_bullhead_catfish,unidentified_fish_fry,sessile_animals,aquatic_worms,sow_bugs,scuds,water_mites,damselfly_nymphs,alderfly_larvae,other_beetles,crayfish,dragonfly_nymphs,riffle_beetles,other_flies,midges,snails,clams,fishfly_larvae,water_penny_beetles,cranefly_larvae,ameletidae,arthropleidae,baetidae,baetiscidae,caenidae,ephemerellidae,ephemeridae,heptageniidae,isonychiidae,leptohyphidae,leptophlebiidae,polymitarcyidae,potamanthidae,pseudironidae,siphlonuridae,capniidae,chloroperlidae,leuctridae,nemouridae,peltoperlidae,perlidae,perlodidae,pteronarcyidae,taeniopterygidae,brachycentridae,dipseuodopsidae,glossosomatidae,goeridae,helicopsychidae,hydropsychidae,hydroptilidae,lepidostomatidae,leptoceridae,limnephilidae,molannidae,odontoceridae,philopotamidae,phryganeidae,polycentropodidae,psychomiidae,rhyacophilidae,uenoidae,total_mayfly,total_stonefly,total_caddisfly,total_ept,hhei_score,hmfei_score,hmfei_narrative) %>% distinct(stream_name, date, .keep_all = TRUE)
phwh_abiotic<- phwh_subset %>%
dplyr::select(stream_name,date,substrate_metric_total,percent_best_types,per_boulder_slabs,per_boulder_256mm,per_bedrock,per_cobble,per_gravel,per_sand,per_silt,per_leaf_woody,per_detritus,per_clay_hardpan,per_muck,per_artificial,pool_metric_total,pool_depth_.cm.,bankfull_metric_total,average_bankfull_.m.,sinuosity,base_flow,date_last_rain,amt_last_rain,X._canopy_open,temp,dissolved_oxygen_mgl,ph,conductivity) %>% distinct(stream_name, date, .keep_all = TRUE)
phwh_salamander<- phwh_subset %>%
dplyr::select(stream_name,date,mountain_dusky_larvae,mountain_dusky_juveniles,mountain_dusky_adults,northern_dusky_larvae,northern_dusky_juveniles,northern_dusky_adults,two_lined_larvae,two_lined_juveniles,two_lined_adults,long_tailed_larvae,long_tailed_juveniles,long_tailed_adults,northern_red_larvae,northern_red_juveniles,northern_red_adults,mole_larvae,mole_juveniles,mole_adults,four_toed_larvae,four_toed_juveniles,four_toed_adults,red_backed_juveniles,red_backed_adults,slimy_juveniles,slimy_adults) %>% replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE)
phwh_fish<- phwh_subset %>%
dplyr::select(stream_name,date,blacknose_dace,bluegill_sunfish,bluntnose_minnow,brindled_madtom,brook_stickleback,brook_trout,central_mudminnow,central_stoneroller_minnow,common_shiner,creek_chub,eastern_sand_darter,fantail_darter,fathead_minnow,golden_shiner,goldfish,grass_pickerel,greenside_darter,green_sunfish,johnny_darter,largemouth_bass,longear_sunfish,longnose_dace,mottled_sculpin,northern_hog_sucker,pumpkinseed_sunfish,rainbow_darter,rainbow_trout,redbelly_dace,redside_dace,river_chub,silverjaw_minnow,smallmouth_bass,spotfin_shiner,stonecat_madtom,striped_shiner,trout_perch,warmouth_sunfish,white_sucker,yellow_bullhead_catfish,unidentified_fish_fry) %>%
replace(is.na(.),0) %>% replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE) %>% dplyr::select(-stream_name,-date)
phwh_fish<-sapply(phwh_fish,as.numeric)
phwh_macro<- phwh_subset %>%
dplyr::select(stream_name,date,sessile_animals,aquatic_worms,sow_bugs,scuds,water_mites,damselfly_nymphs,alderfly_larvae,other_beetles,crayfish,dragonfly_nymphs,riffle_beetles,other_flies,midges,snails,clams,fishfly_larvae,water_penny_beetles,cranefly_larvae,total_mayfly,total_stonefly,total_caddisfly) %>% replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="very abundant",50) %>%
replace(.=="rare", 1) %>% distinct(stream_name, date, .keep_all = TRUE) %>% dplyr::select(-stream_name,-date)
phwh_macro<-sapply(phwh_macro,as.numeric)
phwh_ept<-phwh_subset %>%
dplyr::select(stream_name,date,ameletidae,arthropleidae,baetidae,baetiscidae,caenidae,ephemerellidae,ephemeridae,heptageniidae,isonychiidae,leptohyphidae,leptophlebiidae,polymitarcyidae,potamanthidae,pseudironidae,siphlonuridae,capniidae,chloroperlidae,leuctridae,nemouridae,peltoperlidae,perlidae,perlodidae,pteronarcyidae,taeniopterygidae,brachycentridae,dipseuodopsidae,glossosomatidae,goeridae,helicopsychidae,hydropsychidae,hydroptilidae,lepidostomatidae,leptoceridae,limnephilidae,molannidae,odontoceridae,philopotamidae,phryganeidae,polycentropodidae,psychomiidae,rhyacophilidae,uenoidae) %>%
replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE)
phwh_e<-phwh_subset %>%
dplyr::select(stream_name,date,ameletidae,arthropleidae,baetidae,baetiscidae,caenidae,ephemerellidae,ephemeridae,heptageniidae,isonychiidae,leptohyphidae,leptophlebiidae,polymitarcyidae,potamanthidae,pseudironidae,siphlonuridae) %>% replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE)
phwh_p<-phwh_subset %>%
dplyr::select(stream_name,date,capniidae,chloroperlidae,leuctridae,nemouridae,peltoperlidae,perlidae,perlodidae,pteronarcyidae,taeniopterygidae) %>%
replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE)
phwh_t<-phwh_subset %>%
dplyr::select(stream_name,date,brachycentridae,dipseuodopsidae,glossosomatidae,goeridae,helicopsychidae,hydropsychidae,hydroptilidae,lepidostomatidae,leptoceridae,limnephilidae,molannidae,odontoceridae,philopotamidae,phryganeidae,polycentropodidae,psychomiidae,rhyacophilidae,uenoidae) %>%
replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE)
fish_sum<-phwh_subset$fish_observed
sala_sum<-phwh_subset$salamanders_observed
macro_sum<-phwh_subset$aquatic_macros_observed
frog_sum<-phwh_subset$frogs_tadpoles_observed
total_ept<-phwh_subset$total_ept
hhei<-phwh_subset$hhei_score
hmfei<-phwh_subset$hmfei_score
score<-phwh_subset$hmfei_narrative
reservation<-phwh_subset$reservation
stream_name<-phwh$stream_name
watershed<-phwh$watershed
reservationsum<-phwh_subset %>%
distinct(stream_name, .keep_all = TRUE) %>%
dplyr::select(stream_name,reservation) %>%
arrange(., stream_name)
reservationsum<-na.omit(reservationsum)
reservationcol<-as.numeric(factor(reservationsum$reservation))
scoresum<-phwh_subset %>%
distinct(stream_name, .keep_all = TRUE) %>%
dplyr::select(stream_name,hmfei_narrative) %>%
arrange(., stream_name)
scoresum<-na.omit(scoresum)
scorecol<-as.numeric(factor(scoresum$hmfei_narrative))
streambiotic<-cbind(phwh_salamander,phwh_fish,phwh_macro)
streambioticsum<-streambiotic %>%
group_by(stream_name) %>%
dplyr::select(!date) %>%
summarise_all(list(sum))
streambiotic_nosite<-streambioticsum[-1,-1] %>%
select_if(colSums(.) != 0)
taxatype<-c(rep("salamander",19),rep("fish",20),rep("macro",21))select sites
sitechecks_surveyhistory_rename<-sitechecks_surveyhistory %>% rename(stream_name=stream_names)
sitechecks_surveyhistory_res<-merge(sitechecks_surveyhistory_rename,reservationsum,by="stream_name")
sitechecks_surveyhistory_ressum<-sitechecks_surveyhistory_res %>%
group_by(reservation)
greenlightsites<-read.csv("G:/NaturalResources/Byer/DataAnalysis/PHWH/PHWH_cycle3sites_011323_sites.csv")
sitechecks_surveyhistory_res_suff<-sitechecks_surveyhistory_res[sitechecks_surveyhistory_res$stream_name %in% greenlightsites$stream_nam , ]
phwh_subset_suff<-phwh_subset[phwh_subset$stream_name %in% greenlightsites$stream_nam ,]just salamanders
class(phwh_subset_suff$stream_name)<-"character"
phwh_subset_suff_env_detecthistory<-phwh_subset_suff %>%
dplyr::select(stream_name,reservation,river_basin,latitude,longitude,date,substrate_metric_total,percent_best_types,per_boulder_slabs,per_boulder_256mm,
per_bedrock,per_cobble,per_gravel,per_sand,per_silt,per_leaf_woody,per_detritus,per_clay_hardpan,per_muck,per_artificial,pool_metric_total,
pool_depth_.cm.,bankfull_metric_total,average_bankfull_.m.,sinuosity,X._canopy_open,temp,
dissolved_oxygen_mgl,ph,conductivity,hmfei_narrative) %>%
group_by(stream_name, reservation,date)
phwh_salamander_suff<-phwh_salamander[phwh_salamander$stream_name %in% greenlightsites$stream_nam ,]
phwh_salamander_suff_detecthistory<-phwh_salamander_suff %>%
group_by(stream_name, date)
mtdusky<-phwh_salamander_suff_detecthistory$mountain_dusky_larvae+
phwh_salamander_suff_detecthistory$mountain_dusky_juveniles+
phwh_salamander_suff_detecthistory$mountain_dusky_adults
nodusky<-phwh_salamander_suff_detecthistory$northern_dusky_larvae+
phwh_salamander_suff_detecthistory$northern_dusky_juveniles+
phwh_salamander_suff_detecthistory$northern_dusky_adults
twolined<-phwh_salamander_suff_detecthistory$two_lined_larvae+
phwh_salamander_suff_detecthistory$two_lined_juveniles+
phwh_salamander_suff_detecthistory$two_lined_adults
longtail<-phwh_salamander_suff_detecthistory$long_tailed_larvae+
phwh_salamander_suff_detecthistory$long_tailed_juveniles+
phwh_salamander_suff_detecthistory$long_tailed_adults
red<-phwh_salamander_suff_detecthistory$northern_red_larvae+
phwh_salamander_suff_detecthistory$northern_red_juveniles+
phwh_salamander_suff_detecthistory$northern_red_adults
mole<-phwh_salamander_suff_detecthistory$mole_larvae+
phwh_salamander_suff_detecthistory$mole_juveniles+
phwh_salamander_suff_detecthistory$mole_adults
fourtoed<-phwh_salamander_suff_detecthistory$four_toed_larvae+
phwh_salamander_suff_detecthistory$four_toed_juveniles+
phwh_salamander_suff_detecthistory$four_toed_adults
redback<-phwh_salamander_suff_detecthistory$red_backed_juveniles+
phwh_salamander_suff_detecthistory$red_backed_adults
slimy<-phwh_salamander_suff_detecthistory$slimy_juveniles+
phwh_salamander_suff_detecthistory$slimy_adults
stream_name<-phwh_salamander_suff_detecthistory$stream_name
date<-phwh_salamander_suff_detecthistory$date
phwh_salamander_suff_sum<-data.frame(stream_name,date,
mtdusky,nodusky,twolined,longtail,red,
mole,fourtoed,redback,slimy)
sitechecks<-phwh_salamander_suff_sum[,c(1,2)]
sitechecks$date<- as.Date(sitechecks$date , format = "%Y/%m/%d")
sitechecks$dayofyear<-format(sitechecks$date, "%j")
sitechecks$datecount<-as.numeric(sitechecks$date-min(sitechecks$date)+1)
sitechecks_surveyhistory<-sitechecks %>%
group_by(stream_name) %>%
mutate(check=as.character(dense_rank(date))) %>%
arrange(stream_name) %>%
distinct(stream_name, datecount, .keep_all = TRUE) %>%
spread(check,datecount) %>%
summarise(`1` = mean(`1`,na.rm=T),
`2`=mean(`2`,na.rm=T),
`3`=mean(`3`,na.rm=T),
`4`=mean(`4`,na.rm=T),
`5`=mean(`5`,na.rm=T),
`6`=mean(`6`,na.rm=T),
`7`=mean(`7`,na.rm=T),
`8`=mean(`8`,na.rm=T),
`9` = mean(`9`,na.rm=T),
`10`=mean(`10`,na.rm=T),
`11`=mean(`11`,na.rm=T),
`12`=mean(`12`,na.rm=T),
`13`=mean(`13`,na.rm=T),
`14`=mean(`14`,na.rm=T),
`15`=mean(`15`,na.rm=T),
`16`=mean(`16`,na.rm=T))all taxa
phwh_subset_biotic<-phwh %>%
dplyr::select(stream_name,date,mountain_dusky_larvae,mountain_dusky_juveniles,
mountain_dusky_adults,northern_dusky_larvae,northern_dusky_juveniles,northern_dusky_adults,two_lined_larvae,two_lined_juveniles,
two_lined_adults,long_tailed_larvae,long_tailed_juveniles,long_tailed_adults,northern_red_larvae,northern_red_juveniles,northern_red_adults,
mole_larvae,mole_juveniles,mole_adults,four_toed_larvae,four_toed_juveniles,four_toed_adults,red_backed_juveniles,red_backed_adults,
slimy_juveniles,slimy_adults,blacknose_dace,bluegill_sunfish,bluntnose_minnow,brindled_madtom,brook_stickleback,brook_trout,
central_mudminnow,central_stoneroller_minnow,common_shiner,creek_chub,eastern_sand_darter,fantail_darter,fathead_minnow,golden_shiner,
goldfish,grass_pickerel,greenside_darter,green_sunfish,johnny_darter,largemouth_bass,longear_sunfish,longnose_dace,mottled_sculpin,
northern_hog_sucker,pumpkinseed_sunfish,rainbow_darter,rainbow_trout,redbelly_dace,redside_dace,river_chub,silverjaw_minnow,smallmouth_bass,
spotfin_shiner,stonecat_madtom,striped_shiner,trout_perch,warmouth_sunfish,white_sucker,yellow_bullhead_catfish,unidentified_fish_fry,
sessile_animals,aquatic_worms,sow_bugs,scuds,water_mites,damselfly_nymphs,alderfly_larvae,other_beetles,crayfish,dragonfly_nymphs,
riffle_beetles,other_flies,midges,snails,clams,fishfly_larvae,water_penny_beetles,cranefly_larvae,ameletidae,arthropleidae,baetidae,
baetiscidae,caenidae,ephemerellidae,ephemeridae,heptageniidae,isonychiidae,leptohyphidae,leptophlebiidae,polymitarcyidae,potamanthidae,
pseudironidae,siphlonuridae,capniidae,chloroperlidae,leuctridae,nemouridae,peltoperlidae,perlidae,perlodidae,pteronarcyidae,taeniopterygidae,
brachycentridae,dipseuodopsidae,glossosomatidae,goeridae,helicopsychidae,hydropsychidae,hydroptilidae,lepidostomatidae,leptoceridae,
limnephilidae,molannidae,odontoceridae,philopotamidae,phryganeidae,polycentropodidae,psychomiidae,rhyacophilidae,uenoidae) %>%
distinct(stream_name, date, .keep_all = TRUE)
phwh_subset_biotic_2<-phwh_subset_biotic %>% replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE) %>% dplyr::select(-stream_name,-date)
phwh_subset_biotic_3<-data.frame(sapply(phwh_subset_biotic_2,as.numeric))
phwh_subset_biotic_3$stream_name<-phwh_subset_biotic$stream_name
phwh_subset_biotic_3$date<-phwh_subset_biotic$date
streambiotic<-phwh_subset_biotic_3
streambioticsum<-streambiotic %>%
group_by(stream_name) %>%
dplyr::select(!date) %>%
summarise_all(list(sum))
streambioticsum_suff <-streambioticsum[streambioticsum$stream_name %in% greenlightsites$stream_nam , ]
streambioticsum_suff_mtdusky<-streambioticsum_suff$mountain_dusky_larvae+
streambioticsum_suff$mountain_dusky_juveniles+
streambioticsum_suff$mountain_dusky_adults
streambioticsum_suff_nodusky<-streambioticsum_suff$northern_dusky_larvae+
streambioticsum_suff$northern_dusky_juveniles+
streambioticsum_suff$northern_dusky_adults
streambioticsum_suff_twolined<-streambioticsum_suff$two_lined_larvae+
streambioticsum_suff$two_lined_juveniles+
streambioticsum_suff$two_lined_adults
streambioticsum_suff_longtail<-streambioticsum_suff$long_tailed_larvae+
streambioticsum_suff$long_tailed_juveniles+
streambioticsum_suff$long_tailed_adults
streambioticsum_suff_red<-streambioticsum_suff$northern_red_larvae+
streambioticsum_suff$northern_red_juveniles+
streambioticsum_suff$northern_red_adults
streambioticsum_suff_mole<-streambioticsum_suff$mole_larvae+
streambioticsum_suff$mole_juveniles+
streambioticsum_suff$mole_adults
streambioticsum_suff_fourtoed<-streambioticsum_suff$four_toed_larvae+
streambioticsum_suff$four_toed_juveniles+
streambioticsum_suff$four_toed_adults
streambioticsum_suff_redback<-streambioticsum_suff$red_backed_juveniles+
streambioticsum_suff$red_backed_adults
streambioticsum_suff_slimy<-streambioticsum_suff$slimy_juveniles+
streambioticsum_suff$slimy_adults
streambioticsum_suff_sal<-data.frame(streambioticsum_suff_mtdusky,streambioticsum_suff_nodusky,streambioticsum_suff_twolined,
streambioticsum_suff_longtail,streambioticsum_suff_red,streambioticsum_suff_mole,
streambioticsum_suff_fourtoed,streambioticsum_suff_redback,streambioticsum_suff_slimy)
streambioticsum_suff_fish<-streambioticsum_suff[,27:65]
streambioticsum_suff_macro<-streambioticsum_suff[,67:126]
streambioticsum_suff_biotic<-data.frame(streambioticsum_suff_sal,streambioticsum_suff_fish,streambioticsum_suff_macro)
streambioticsum_suff_biotic_bin<- streambioticsum_suff_biotic %>% mutate_if(is.numeric, ~1 * (. >= 1))make new dataset for occupancy
streambiotic_suff<-phwh_subset_biotic_3[phwh_subset_biotic_3$stream_name %in% greenlightsites$stream_nam , ]
streambiotic_suff_mtdusky<-streambiotic_suff$mountain_dusky_larvae+
streambiotic_suff$mountain_dusky_juveniles+
streambiotic_suff$mountain_dusky_adults
streambiotic_suff_nodusky<-streambiotic_suff$northern_dusky_larvae+
streambiotic_suff$northern_dusky_juveniles+
streambiotic_suff$northern_dusky_adults
streambiotic_suff_twolined<-streambiotic_suff$two_lined_larvae+
streambiotic_suff$two_lined_juveniles+
streambiotic_suff$two_lined_adults
streambiotic_suff_longtail<-streambiotic_suff$long_tailed_larvae+
streambiotic_suff$long_tailed_juveniles+
streambiotic_suff$long_tailed_adults
streambiotic_suff_red<-streambiotic_suff$northern_red_larvae+
streambiotic_suff$northern_red_juveniles+
streambiotic_suff$northern_red_adults
streambiotic_suff_mole<-streambiotic_suff$mole_larvae+
streambiotic_suff$mole_juveniles+
streambiotic_suff$mole_adults
streambiotic_suff_fourtoed<-streambiotic_suff$four_toed_larvae+
streambiotic_suff$four_toed_juveniles+
streambiotic_suff$four_toed_adults
streambiotic_suff_redback<-streambiotic_suff$red_backed_juveniles+
streambiotic_suff$red_backed_adults
streambiotic_suff_slimy<-streambiotic_suff$slimy_juveniles+
streambiotic_suff$slimy_adults
streambiotic_suff_sal<-data.frame(streambiotic_suff_mtdusky,streambiotic_suff_nodusky,streambiotic_suff_twolined,
streambiotic_suff_longtail,streambiotic_suff_red,streambiotic_suff_mole,
streambiotic_suff_fourtoed,streambiotic_suff_redback,streambiotic_suff_slimy)
streambiotic_suff_fish<-streambiotic_suff[,26:64]
streambiotic_suff_macro<-streambiotic_suff[,66:125]
streambiotic_suff_biotic<-data.frame(streambiotic_suff_sal,streambiotic_suff_fish,streambiotic_suff_macro)
streambiotic_suff_biotic_bin<- streambiotic_suff_biotic %>% mutate_if(is.numeric, ~1 * (. >= 1))
phwh_alltax_suff_env_detecthistory<-cbind(phwh_subset_suff_env_detecthistory,streambiotic_suff_biotic_bin)
phwh_alltax_suff_env_detecthistory$date<- as.Date(phwh_alltax_suff_env_detecthistory$date , format = "%Y/%m/%d")
phwh_alltax_suff_env_detecthistory$datecount<-as.numeric(phwh_alltax_suff_env_detecthistory$date-min(phwh_alltax_suff_env_detecthistory$date)+1)
phwh_alltax_suff_env_detecthistory$dayofyear<-format(phwh_alltax_suff_env_detecthistory$date, "%j")
phwh_alltax_suff_env_detecthistory<-phwh_alltax_suff_env_detecthistory %>%
dplyr::select(!c(latitude,longitude))
phwh_alltax_suff_env_detecthistory_firstcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[1]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
firstcheck_all<-phwh_alltax_suff_env_detecthistory_firstcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_secondcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[2]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
secondcheck_all<-phwh_alltax_suff_env_detecthistory_secondcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_thirdcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[3]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
thirdcheck_all<-phwh_alltax_suff_env_detecthistory_thirdcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_fourthcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[4]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
fourthcheck_all<-phwh_alltax_suff_env_detecthistory_fourthcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_fifthcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[5]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
fifthcheck_all<-phwh_alltax_suff_env_detecthistory_fifthcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_sixthcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[6]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
sixthcheck_all<-phwh_alltax_suff_env_detecthistory_sixthcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_seventhcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[7]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
seventhcheck_all<-phwh_alltax_suff_env_detecthistory_seventhcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
seventhcheck_all_2<-phwh_alltax_suff_env_detecthistory_seventhcheck %>%
dplyr::select(stream_name,streambiotic_suff_mtdusky:uenoidae) %>%
pivot_longer(-stream_name) %>%
pivot_wider(names_from=stream_name) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_eighthcheck<- phwh_alltax_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[8]))) %>%
unnest(cols = c(reservation, river_basin, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
eighthcheck_all<-phwh_alltax_suff_env_detecthistory_eighthcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwhall_occobject<-list()
phwhall_occobject$y<-array(dim=c(108,137,8))
phwhall_occobject$y <- array(c(as.matrix(firstcheck_all),as.matrix(secondcheck_all),
as.matrix(thirdcheck_all),as.matrix(fourthcheck_all),
as.matrix(fifthcheck_all),as.matrix(sixthcheck_all),
as.matrix(seventhcheck_all),as.matrix(eighthcheck_all)), dim=c(108,137,8))
phwhall_occobject$det.covs$day<-as.matrix(sitechecks_surveyhistory[,2:9])
seasons<-round(sitechecks_surveyhistory[,2:9]/365)+1
dayyear<-cbind(as.numeric(phwh_alltax_suff_env_detecthistory_firstcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_secondcheck$dayofyear),
as.numeric(phwh_alltax_suff_env_detecthistory_thirdcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_fourthcheck$dayofyear),
as.numeric(phwh_alltax_suff_env_detecthistory_fifthcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_sixthcheck$dayofyear),
as.numeric(phwh_alltax_suff_env_detecthistory_seventhcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_eighthcheck$dayofyear))
phwhall_occobject$det.covs$seasons<-as.matrix(seasons)
phwhall_occobject$det.covs$dayyear<-as.matrix(dayyear)
occcov<-phwh_subset_suff_env_detecthistory %>% group_by(stream_name) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
occcov_2<-occcov[,-1:-4]
cols<-seq(1,23,1)
occcov_2[,cols] = apply(occcov_2[,cols], 2, function(x) as.numeric(as.character(x)))
occcov_3<-occcov_2[ , colSums(is.na(occcov_2)) == 0]
phwhall_occobject$occ.covs<-as.matrix(occcov_3)
library(rgdal)
d <- data.frame(lon=occcov$longitude, lat=occcov$latitude)
coordinates(d) <- c("lon", "lat")
proj4string(d) <- CRS("+init=epsg:4326") # WGS 84
CRS.new <- CRS("+init=epsg:3734")
d.3734 <- spTransform(d, CRS.new)
plot(d)
projcoords<-data.frame(d.3734)
phwhall_occobject$coords<-as.matrix(projcoords)
phwhall_occobject$occ.covs## substrate_metric_total percent_best_types per_boulder_slabs per_boulder_256mm per_bedrock per_cobble per_gravel per_sand per_silt per_leaf_woody per_detritus per_clay_hardpan per_muck per_artificial pool_metric_total pool_depth_.cm. bankfull_metric_total average_bankfull_.m. X._canopy_open
## [1,] 22.33333 9.000000 0.0000000 1.6666667 0.0000000 7.3333333 45.000000 30.666667 0.6666667 5.0000000 0.6666667 7.3333333 0.0000000 1.6666667 20.00000 51.066667 30.000000 5.243333 33.00000
## [2,] 27.50000 42.000000 0.0000000 2.0000000 35.5000000 4.5000000 29.000000 20.000000 1.0000000 5.5000000 0.0000000 2.5000000 0.0000000 0.0000000 27.50000 18.100000 17.500000 1.480000 15.50000
## [3,] 25.00000 36.000000 0.3333333 7.0000000 19.6666667 9.0000000 33.333333 13.000000 10.3333333 5.0000000 0.0000000 1.6666667 0.0000000 0.6666667 23.33333 36.800000 26.666667 3.753333 21.66667
## [4,] 21.33333 19.333333 2.3333333 5.0000000 0.0000000 12.0000000 40.000000 20.000000 2.6666667 2.0000000 0.0000000 9.0000000 1.0000000 6.0000000 23.33333 46.200000 28.333333 5.043333 21.33333
## [5,] 32.33333 66.333333 0.0000000 14.0000000 47.6666667 4.6666667 17.333333 9.000000 2.6666667 3.0000000 0.0000000 1.6666667 0.0000000 0.0000000 25.00000 14.066667 20.000000 1.993333 14.33333
## [6,] 11.00000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 5.000000 6.000000 35.0000000 36.5000000 0.0000000 10.0000000 7.5000000 0.0000000 12.50000 10.000000 15.000000 1.350000 19.00000
## [7,] 20.00000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 50.000000 30.000000 1.0000000 1.0000000 0.0000000 18.0000000 0.0000000 0.0000000 20.00000 38.100000 25.000000 3.750000 20.00000
## [8,] 22.50000 10.500000 0.0000000 0.5000000 0.0000000 10.0000000 36.000000 22.500000 3.5000000 10.0000000 1.5000000 15.5000000 0.5000000 0.0000000 20.00000 40.850000 22.500000 2.850000 22.00000
## [9,] 18.00000 7.000000 0.0000000 3.6666667 0.6666667 2.6666667 15.333333 26.666667 23.3333333 2.0000000 0.3333333 17.6666667 7.0000000 0.0000000 20.00000 15.333333 25.000000 3.446667 17.00000
## [10,] 28.00000 24.666667 0.0000000 8.0000000 10.0000000 6.6666667 31.666667 20.000000 4.6666667 5.6666667 0.0000000 13.3333333 0.0000000 0.0000000 23.33333 17.000000 18.333333 1.566667 15.66667
## [11,] 23.53333 14.000000 0.0000000 1.6666667 3.2000000 9.8666667 44.266667 23.600000 1.7333333 6.6000000 0.4666667 5.9333333 0.6666667 0.0000000 23.66667 30.040000 27.666667 4.108000 18.73333
## [12,] 21.68750 11.312500 0.0312500 1.2187500 0.0000000 10.0625000 36.937500 27.187500 5.4062500 3.5312500 0.6875000 14.0625000 0.8750000 0.0000000 23.12500 32.393750 27.187500 4.171875 22.56250
## [13,] 21.50000 15.000000 0.0000000 0.0000000 0.0000000 15.0000000 42.500000 26.000000 1.5000000 2.5000000 0.5000000 12.0000000 0.0000000 0.0000000 20.00000 47.750000 22.500000 2.680000 18.00000
## [14,] 22.50000 20.000000 0.0000000 1.5000000 0.0000000 18.5000000 38.000000 24.500000 4.5000000 4.5000000 0.5000000 8.0000000 0.0000000 0.0000000 30.00000 25.000000 20.000000 2.610000 17.00000
## [15,] 30.33333 47.000000 2.6666667 2.0000000 20.0000000 22.3333333 33.666667 13.000000 1.0000000 3.3333333 0.0000000 2.0000000 0.0000000 0.0000000 25.00000 33.866667 26.666667 3.966667 26.00000
## [16,] 34.50000 65.000000 6.5000000 5.0000000 25.0000000 28.5000000 21.500000 6.000000 1.0000000 5.5000000 0.0000000 1.0000000 0.0000000 0.0000000 25.00000 21.000000 17.500000 1.850000 22.50000
## [17,] 22.66667 29.666667 4.0000000 3.3333333 3.6666667 18.6666667 36.666667 10.000000 5.0000000 6.0000000 0.0000000 12.6666667 0.0000000 0.0000000 26.66667 18.166667 20.000000 1.743333 17.00000
## [18,] 36.00000 65.000000 14.2500000 3.2500000 23.0000000 24.5000000 16.750000 8.750000 2.5000000 5.5000000 0.0000000 1.5000000 0.0000000 0.0000000 25.00000 25.200000 21.250000 2.575000 25.75000
## [19,] 14.00000 16.500000 0.0000000 5.5000000 0.0000000 11.0000000 20.000000 8.000000 9.0000000 3.5000000 4.0000000 20.5000000 9.0000000 9.5000000 27.50000 22.250000 17.500000 1.400000 36.50000
## [20,] 23.33333 23.666667 1.0000000 3.3333333 0.0000000 19.3333333 27.333333 19.333333 6.3333333 8.6666667 0.0000000 14.6666667 0.0000000 0.0000000 20.00000 37.833333 23.333333 2.866667 21.66667
## [21,] 15.00000 7.000000 0.3333333 0.6666667 0.0000000 6.0000000 14.666667 12.333333 9.6666667 4.6666667 0.0000000 51.6666667 0.0000000 0.0000000 16.66667 11.233333 20.000000 2.086667 21.33333
## [22,] 28.50000 45.750000 4.2500000 3.0000000 17.0000000 21.5000000 24.250000 10.750000 7.2500000 5.0000000 0.0000000 6.5000000 0.5000000 0.0000000 21.25000 27.825000 26.250000 4.090000 15.25000
## [23,] 32.33333 51.333333 10.3333333 3.3333333 17.3333333 20.3333333 31.000000 11.000000 3.3333333 2.6666667 0.0000000 0.6666667 0.0000000 0.0000000 23.33333 19.133333 25.000000 3.500000 14.50000
## [24,] 27.00000 33.500000 5.0000000 1.3333333 4.8333333 22.3333333 41.333333 15.333333 3.3333333 2.3333333 0.0000000 4.1666667 0.0000000 0.0000000 23.33333 28.433333 25.000000 3.293333 18.66667
## [25,] 27.00000 31.666667 6.3333333 1.0000000 6.6666667 17.6666667 41.666667 15.333333 2.3333333 3.3333333 0.3333333 5.3333333 0.0000000 0.0000000 23.33333 32.033333 20.000000 2.623333 16.00000
## [26,] 18.75000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 30.000000 43.750000 11.0000000 4.2500000 0.2500000 10.7500000 0.0000000 0.0000000 22.50000 34.500000 18.750000 1.970000 41.25000
## [27,] 33.66667 66.333333 10.3333333 4.3333333 37.6666667 14.0000000 19.666667 7.333333 2.6666667 2.3333333 0.0000000 1.6666667 0.0000000 0.0000000 21.66667 15.300000 20.000000 2.466667 13.33333
## [28,] 20.66667 19.000000 1.0000000 3.0000000 7.6666667 7.3333333 38.666667 22.666667 4.6666667 2.0000000 0.0000000 12.6666667 0.3333333 0.0000000 28.33333 23.300000 23.333333 3.663333 17.33333
## [29,] 22.00000 14.500000 0.0000000 0.7500000 0.0000000 13.7500000 32.750000 23.750000 10.0000000 3.5000000 0.0000000 15.2500000 0.2500000 0.0000000 22.50000 50.275000 22.500000 3.212500 22.00000
## [30,] 24.00000 10.000000 0.0000000 1.3333333 0.0000000 8.6666667 41.666667 26.666667 2.6666667 6.0000000 0.0000000 13.0000000 0.0000000 0.0000000 20.00000 69.766667 25.000000 3.453333 18.00000
## [31,] 33.00000 55.333333 1.6666667 2.6666667 38.6666667 12.3333333 35.666667 7.000000 0.6666667 1.3333333 0.0000000 0.0000000 0.0000000 0.0000000 23.33333 24.533333 26.666667 4.796667 28.66667
## [32,] 28.00000 37.666667 1.0000000 4.0000000 18.0000000 14.6666667 43.333333 14.000000 1.0000000 1.6666667 0.0000000 2.3333333 0.0000000 0.0000000 23.33333 59.133333 23.333333 3.243333 26.00000
## [33,] 21.33333 5.333333 0.0000000 0.3333333 0.0000000 5.0000000 45.000000 31.666667 6.6666667 3.6666667 0.0000000 7.6666667 0.0000000 0.0000000 20.00000 38.966667 20.000000 2.370000 17.00000
## [34,] 15.00000 1.000000 0.0000000 0.3333333 0.0000000 0.6666667 20.000000 39.333333 8.3333333 3.0000000 0.6666667 26.6666667 1.0000000 0.0000000 18.33333 29.766667 18.333333 1.766667 18.00000
## [35,] 24.00000 24.000000 0.0000000 1.0000000 0.0000000 23.0000000 40.000000 22.000000 1.6666667 4.0000000 0.0000000 8.3333333 0.0000000 0.0000000 21.66667 28.100000 21.666667 2.670000 48.33333
## [36,] 19.00000 13.250000 0.0000000 1.2500000 0.0000000 12.0000000 45.000000 18.000000 1.2500000 1.5000000 0.0000000 21.0000000 0.0000000 0.0000000 25.00000 35.700000 20.000000 2.350000 17.50000
## [37,] 21.00000 17.000000 0.0000000 2.0000000 0.0000000 15.0000000 45.000000 25.000000 0.0000000 8.0000000 0.0000000 5.0000000 0.0000000 0.0000000 20.00000 45.000000 30.000000 4.230000 40.00000
## [38,] 27.33333 36.000000 1.3333333 3.6666667 16.6666667 14.3333333 36.333333 18.666667 2.3333333 5.0000000 0.0000000 1.6666667 0.0000000 0.0000000 21.66667 10.300000 18.333333 1.866667 23.00000
## [39,] 22.33333 14.333333 0.0000000 7.0000000 0.0000000 7.3333333 38.333333 26.666667 3.0000000 4.6666667 0.3333333 9.3333333 0.0000000 0.0000000 20.00000 40.000000 20.000000 2.336667 17.66667
## [40,] 18.33333 5.000000 0.0000000 1.0000000 0.0000000 4.0000000 26.666667 29.000000 4.6666667 7.6666667 0.0000000 27.0000000 0.0000000 0.0000000 26.66667 18.933333 18.333333 1.836667 28.00000
## [41,] 21.00000 17.333333 0.3333333 5.6666667 0.0000000 11.3333333 43.333333 21.000000 2.6666667 2.6666667 0.3333333 11.6666667 1.0000000 0.0000000 23.33333 40.266667 20.000000 2.493333 21.33333
## [42,] 20.66667 15.000000 0.3333333 6.3333333 0.0000000 8.3333333 31.333333 24.333333 4.3333333 7.0000000 1.6666667 15.3333333 1.0000000 0.0000000 25.00000 14.133333 18.333333 1.646667 18.33333
## [43,] 21.50000 10.000000 0.0000000 3.7500000 0.0000000 6.2500000 32.000000 37.250000 5.7500000 4.5000000 0.0000000 10.5000000 0.0000000 0.0000000 22.50000 27.875000 20.000000 2.305000 20.00000
## [44,] 20.00000 10.500000 0.0000000 1.5000000 0.0000000 9.0000000 40.666667 24.666667 2.0000000 4.0000000 0.1666667 18.0000000 0.0000000 0.0000000 23.33333 21.000000 20.000000 2.373333 18.66667
## [45,] 30.50000 60.625000 8.7500000 3.2500000 28.7500000 19.8750000 28.000000 7.000000 1.1250000 2.7500000 0.5000000 0.0000000 0.0000000 0.0000000 26.25000 23.925000 27.500000 3.920000 18.75000
## [46,] 31.25000 49.500000 4.7500000 6.2500000 23.5000000 15.0000000 33.250000 10.000000 2.8750000 2.8750000 0.1250000 1.3750000 0.0000000 0.0000000 26.25000 32.350000 28.750000 4.205000 17.75000
## [47,] 34.25000 75.750000 4.7500000 5.0000000 55.5000000 10.5000000 15.750000 4.750000 0.3750000 2.8750000 0.0000000 0.5000000 0.0000000 0.0000000 22.50000 14.375000 22.500000 2.915000 17.75000
## [48,] 33.25000 56.000000 4.7500000 4.7500000 31.2500000 21.2500000 26.000000 6.250000 1.7500000 1.1250000 0.1250000 2.7500000 0.0000000 0.0000000 27.50000 22.975000 22.500000 2.872500 18.50000
## [49,] 25.40000 28.600000 0.0000000 4.0000000 0.0000000 24.6000000 40.600000 21.000000 1.6000000 3.4000000 0.2000000 4.6000000 0.0000000 0.0000000 22.00000 43.980000 27.000000 4.478000 34.80000
## [50,] 29.50000 42.500000 0.5000000 20.0000000 9.5000000 12.5000000 17.500000 27.500000 2.0000000 3.0000000 0.5000000 7.0000000 0.0000000 0.0000000 22.50000 28.500000 17.500000 1.540000 16.00000
## [51,] 19.33333 18.333333 0.3333333 11.6666667 0.0000000 6.3333333 21.666667 35.000000 1.0000000 7.6666667 0.3333333 16.0000000 0.0000000 0.0000000 15.00000 8.300000 16.666667 1.390000 17.33333
## [52,] 19.00000 9.333333 0.0000000 5.0000000 0.0000000 4.3333333 21.000000 30.666667 3.3333333 5.3333333 0.0000000 30.3333333 0.0000000 0.0000000 26.66667 18.400000 8.333333 1.060000 19.66667
## [53,] 29.62500 45.875000 1.3750000 11.1250000 10.5000000 22.8750000 33.625000 14.000000 1.1875000 3.3125000 0.1250000 1.8125000 0.0625000 0.0000000 25.62500 22.300000 26.250000 4.570000 28.50000
## [54,] 38.14286 80.214286 0.0000000 9.0000000 66.2857143 4.9285714 5.571429 7.857143 2.2142857 3.0000000 0.4285714 0.1428571 0.5714286 0.0000000 25.00000 20.642857 24.285714 3.270000 20.00000
## [55,] 21.66667 18.666667 1.6666667 4.3333333 0.0000000 13.0000000 25.000000 36.666667 0.6666667 6.3333333 1.6666667 10.6666667 0.0000000 0.0000000 11.66667 4.526667 15.000000 1.273333 15.33333
## [56,] 19.33333 6.000000 0.0000000 2.5000000 0.0000000 3.5000000 29.000000 34.000000 1.3333333 3.6666667 1.0000000 25.0000000 0.0000000 0.0000000 18.33333 8.733333 15.000000 1.313333 24.66667
## [57,] 18.50000 9.250000 0.0000000 4.5000000 0.0000000 4.7500000 25.750000 27.500000 4.0000000 4.0000000 0.7500000 22.0000000 0.0000000 0.0000000 18.75000 24.975000 21.250000 2.467500 20.75000
## [58,] 19.66667 5.666667 0.0000000 1.3333333 0.0000000 4.3333333 25.666667 44.666667 3.3333333 3.3333333 2.3333333 15.0000000 0.0000000 0.0000000 21.66667 23.066667 20.000000 2.020000 17.66667
## [59,] 10.75000 4.000000 0.0000000 2.5000000 0.0000000 1.5250000 6.750000 23.875000 2.4750000 6.6250000 4.2500000 52.0000000 0.0000000 0.0000000 11.25000 6.300000 10.000000 1.107500 19.00000
## [60,] 15.00000 11.250000 0.7500000 3.7500000 0.0000000 6.7500000 10.500000 32.000000 1.7500000 6.2500000 2.7500000 35.5000000 0.0000000 0.0000000 8.75000 5.475000 12.500000 1.080000 17.75000
## [61,] 22.66667 28.000000 1.3333333 9.0000000 2.6666667 15.0000000 22.333333 42.333333 0.5000000 2.5000000 2.5000000 1.8333333 0.0000000 0.0000000 20.00000 13.233333 11.666667 1.226667 19.33333
## [62,] 22.33333 12.333333 0.1666667 0.5000000 0.0000000 11.6666667 41.000000 24.666667 2.0000000 4.3333333 0.6666667 14.6666667 0.3333333 0.0000000 20.00000 43.000000 28.333333 3.743333 21.00000
## [63,] 29.00000 47.666667 7.0000000 6.3333333 1.3333333 33.0000000 26.000000 16.000000 1.0000000 3.6666667 0.3333333 5.3333333 0.0000000 0.0000000 21.66667 30.900000 18.333333 1.420000 15.33333
## [64,] 6.00000 0.750000 0.0000000 0.2500000 0.0000000 0.5000000 3.250000 2.000000 0.5000000 11.0000000 3.0000000 79.5000000 0.0000000 0.0000000 7.50000 4.000000 10.000000 1.085000 15.00000
## [65,] 26.33333 32.333333 1.3333333 4.0000000 13.6666667 13.3333333 19.000000 29.666667 3.3333333 6.3333333 2.6666667 6.6666667 0.0000000 0.0000000 11.66667 7.333333 13.333333 1.323333 20.33333
## [66,] 3.50000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 0.250000 0.000000 2.2500000 3.5000000 0.0000000 94.0000000 0.0000000 0.0000000 15.00000 9.000000 10.000000 1.125000 67.50000
## [67,] 19.33333 2.666667 0.0000000 0.3333333 0.0000000 2.3333333 32.666667 32.666667 3.0000000 3.3333333 0.0000000 25.6666667 0.0000000 0.0000000 20.00000 39.333333 21.666667 2.736667 19.33333
## [68,] 19.00000 6.300000 0.0000000 0.7000000 0.0000000 5.6000000 24.800000 44.800000 3.4000000 4.4000000 0.3000000 15.4000000 0.6000000 0.0000000 23.00000 20.840000 16.000000 1.740000 19.40000
## [69,] 22.66667 17.666667 0.6666667 4.3333333 0.0000000 12.6666667 30.000000 23.333333 5.0000000 5.0000000 1.6666667 17.3333333 0.0000000 0.0000000 15.00000 8.796667 18.333333 1.563333 17.66667
## [70,] 20.00000 2.666667 0.0000000 1.0000000 0.0000000 1.6666667 27.000000 27.666667 7.3333333 9.6666667 0.0000000 25.6666667 0.0000000 0.0000000 21.66667 10.800000 11.666667 1.026667 15.33333
## [71,] 23.00000 15.250000 0.2500000 8.5000000 0.0000000 6.5000000 27.500000 37.500000 3.0000000 3.7500000 1.0000000 11.5000000 0.0000000 0.0000000 15.00000 6.550000 10.000000 1.005000 16.50000
## [72,] 20.33333 9.666667 0.0000000 0.6666667 0.0000000 9.0000000 29.666667 28.333333 5.0000000 2.0000000 0.3333333 22.6666667 2.3333333 0.0000000 23.33333 17.133333 16.666667 1.843333 16.33333
## [73,] 22.33333 4.666667 0.0000000 1.0000000 0.0000000 3.6666667 41.666667 28.333333 2.6666667 6.3333333 0.0000000 16.0000000 0.3333333 0.0000000 20.00000 34.166667 21.666667 3.076667 15.33333
## [74,] 20.66667 22.000000 0.0000000 8.0000000 0.0000000 14.0000000 29.333333 30.000000 3.6666667 1.3333333 0.0000000 12.0000000 0.0000000 0.0000000 26.66667 18.233333 18.333333 1.690000 26.00000
## [75,] 19.50000 13.500000 0.0000000 8.0000000 0.0000000 5.5000000 22.500000 25.000000 2.5000000 3.0000000 0.0000000 32.0000000 1.5000000 0.0000000 27.50000 23.150000 12.500000 1.240000 22.00000
## [76,] 22.50000 14.500000 0.0000000 4.5000000 0.0000000 10.0000000 38.500000 29.000000 2.0000000 1.0000000 0.0000000 14.0000000 1.0000000 0.0000000 25.00000 19.250000 22.500000 2.505000 19.00000
## [77,] 22.33333 17.000000 0.0000000 7.6666667 0.0000000 9.3333333 30.333333 32.333333 5.3333333 4.0000000 1.0000000 10.0000000 0.0000000 0.0000000 18.33333 10.100000 18.333333 1.840000 16.00000
## [78,] 22.00000 17.333333 0.3333333 3.0000000 0.0000000 14.0000000 39.000000 31.000000 1.6666667 3.0000000 0.0000000 8.0000000 0.0000000 0.0000000 20.00000 42.733333 26.666667 3.540000 18.66667
## [79,] 22.33333 14.333333 0.6666667 2.3333333 0.0000000 11.3333333 43.333333 32.666667 1.3333333 2.6666667 0.0000000 5.0000000 0.0000000 0.0000000 25.00000 17.733333 20.000000 2.300000 18.00000
## [80,] 22.00000 10.500000 0.0000000 4.0000000 0.0000000 6.5000000 37.500000 39.500000 2.0000000 6.0000000 1.0000000 3.5000000 0.0000000 0.0000000 22.50000 15.050000 20.000000 1.715000 20.00000
## [81,] 12.00000 4.000000 0.0000000 1.0000000 0.0000000 3.0000000 18.000000 40.000000 0.0000000 8.0000000 0.0000000 30.0000000 0.0000000 0.0000000 0.00000 0.000000 15.000000 1.150000 18.00000
## [82,] 21.50000 3.500000 0.0000000 1.0000000 0.0000000 2.5000000 39.500000 37.500000 0.5000000 4.5000000 0.0000000 14.5000000 0.0000000 0.0000000 15.00000 6.100000 17.500000 1.470000 19.50000
## [83,] 23.00000 4.500000 0.0000000 1.5000000 0.0000000 3.0000000 38.000000 33.000000 4.0000000 9.0000000 2.5000000 8.5000000 0.5000000 0.0000000 20.00000 13.800000 15.000000 1.330000 17.00000
## [84,] 19.00000 2.500000 0.0000000 0.5000000 0.0000000 2.0000000 30.000000 32.500000 3.5000000 2.5000000 0.5000000 28.5000000 0.0000000 0.0000000 30.00000 28.450000 15.000000 1.335000 32.50000
## [85,] 17.50000 2.500000 0.0000000 0.5000000 0.0000000 2.0000000 24.500000 37.000000 2.0000000 4.0000000 0.5000000 29.5000000 0.0000000 0.0000000 27.50000 22.650000 17.500000 1.915000 36.50000
## [86,] 22.00000 12.000000 0.0000000 1.5000000 0.0000000 10.5000000 44.000000 28.000000 2.5000000 5.5000000 0.0000000 8.0000000 0.0000000 0.0000000 20.00000 52.800000 30.000000 5.950000 37.50000
## [87,] 21.33333 1.000000 0.0000000 0.0000000 0.0000000 1.0000000 37.333333 33.000000 5.6666667 4.6666667 1.6666667 9.6666667 1.3333333 0.0000000 21.66667 31.666667 21.666667 2.933333 31.00000
## [88,] 31.66667 56.333333 0.0000000 1.6666667 43.0000000 11.6666667 28.333333 11.666667 0.6666667 3.0000000 0.0000000 0.0000000 0.0000000 0.0000000 25.00000 13.800000 26.666667 4.233333 21.66667
## [89,] 22.66667 33.666667 0.0000000 4.0000000 15.6666667 14.0000000 28.333333 23.666667 2.6666667 8.3333333 0.0000000 3.3333333 0.0000000 0.0000000 20.00000 51.100000 28.333333 4.330000 19.00000
## [90,] 13.66667 1.000000 0.0000000 0.0000000 1.0000000 0.0000000 20.666667 15.000000 7.3333333 12.0000000 1.0000000 41.6666667 1.3333333 0.0000000 23.33333 14.466667 16.666667 1.346667 20.33333
## [91,] 24.50000 21.500000 0.0000000 1.0000000 0.0000000 20.5000000 45.000000 22.500000 1.5000000 0.5000000 0.0000000 9.0000000 0.0000000 0.0000000 22.50000 34.900000 15.000000 1.300000 56.50000
## [92,] 31.75000 67.750000 0.0000000 1.2500000 63.5000000 3.0000000 14.750000 11.250000 2.7500000 2.5000000 0.2500000 0.7500000 0.0000000 0.0000000 25.00000 18.175000 26.250000 3.812500 18.00000
## [93,] 26.66667 27.000000 1.0000000 1.6666667 21.3333333 3.0000000 40.333333 19.666667 1.0000000 4.3333333 0.3333333 7.0000000 0.3333333 0.0000000 28.33333 19.033333 18.333333 1.800000 18.00000
## [94,] 23.33333 17.333333 0.3333333 1.6666667 6.0000000 9.3333333 37.666667 27.333333 1.6666667 7.3333333 0.3333333 8.3333333 0.0000000 0.0000000 25.00000 14.500000 15.000000 1.380000 17.00000
## [95,] 23.00000 12.375000 0.0000000 1.1250000 3.2500000 8.0000000 41.250000 35.250000 1.2500000 4.3750000 0.5000000 5.0000000 0.0000000 0.0000000 25.00000 31.225000 25.000000 3.317500 23.25000
## [96,] 22.00000 4.000000 0.0000000 0.5000000 0.0000000 3.5000000 45.500000 28.000000 3.5000000 7.0000000 2.0000000 9.5000000 0.0000000 0.5000000 25.00000 31.650000 20.000000 2.545000 16.50000
## [97,] 20.50000 0.500000 0.0000000 0.5000000 0.0000000 0.0000000 29.500000 46.000000 2.5000000 5.5000000 1.0000000 15.0000000 0.0000000 0.0000000 25.00000 16.550000 20.000000 1.635000 24.00000
## [98,] 26.00000 23.500000 1.0000000 1.0000000 1.5000000 20.0000000 39.000000 21.500000 1.0000000 6.0000000 0.5000000 8.5000000 0.0000000 0.0000000 22.50000 35.300000 30.000000 5.600000 40.00000
## [99,] 33.00000 41.333333 0.6666667 1.0000000 33.0000000 9.3333333 37.666667 12.000000 3.0000000 2.5000000 0.1666667 0.5000000 0.1666667 0.0000000 25.00000 14.533333 28.333333 3.930000 19.66667
## [100,] 21.66667 13.333333 0.3333333 0.3333333 0.0000000 12.6666667 46.333333 24.666667 1.6666667 3.0000000 0.0000000 11.0000000 0.0000000 0.0000000 25.00000 17.700000 21.666667 2.933333 21.00000
## [101,] 28.00000 26.500000 4.0000000 3.0000000 9.5000000 10.0000000 50.500000 9.500000 0.5000000 7.0000000 0.0000000 6.0000000 0.0000000 0.0000000 7.50000 5.000000 25.000000 3.755000 19.50000
## [102,] 31.66667 42.000000 1.3333333 3.0000000 22.6666667 15.0000000 35.333333 13.333333 1.3333333 5.3333333 0.3333333 2.3333333 0.0000000 0.0000000 30.00000 26.600000 25.000000 4.560000 22.00000
## [103,] 28.33333 52.666667 8.0000000 3.0000000 12.0000000 29.6666667 34.000000 11.333333 0.6666667 1.3333333 0.0000000 0.0000000 0.0000000 0.0000000 20.00000 63.600000 30.000000 4.560000 34.00000
## [104,] 22.33333 14.666667 0.6666667 0.3333333 1.6666667 12.0000000 43.666667 25.000000 0.6666667 5.0000000 0.6666667 10.3333333 0.0000000 0.0000000 26.66667 21.033333 23.333333 3.053333 20.33333
## [105,] 27.50000 39.000000 0.0000000 3.0000000 32.0000000 4.0000000 28.000000 17.500000 1.0000000 5.0000000 0.0000000 9.5000000 0.0000000 0.0000000 25.00000 13.600000 17.500000 1.425000 20.00000
## [106,] 32.50000 55.500000 2.5000000 1.0000000 40.0000000 12.0000000 31.500000 9.000000 0.5000000 2.0000000 0.0000000 1.5000000 0.0000000 0.0000000 25.00000 35.500000 22.500000 2.625000 17.00000
## [107,] 34.66667 60.833333 0.0000000 0.8333333 56.6666667 3.3333333 19.000000 11.666667 1.1666667 2.8333333 0.1666667 4.3333333 0.0000000 0.0000000 11.66667 6.233333 18.333333 1.940000 16.66667
## [108,] 27.00000 44.875000 0.0000000 12.5000000 8.3750000 24.0000000 32.375000 13.625000 2.8750000 1.1250000 0.3750000 4.0000000 0.0000000 0.7500000 23.75000 31.437500 27.500000 4.882500 51.25000
## [109,] 23.00000 17.000000 0.0000000 4.0000000 6.0000000 7.0000000 43.000000 29.000000 2.0000000 2.0000000 0.0000000 7.0000000 0.0000000 0.0000000 25.00000 12.000000 25.000000 3.930000 15.00000
## [110,] 34.00000 41.000000 0.0000000 10.0000000 21.0000000 10.0000000 26.000000 9.000000 2.0000000 9.0000000 1.0000000 12.0000000 0.0000000 0.0000000 25.00000 20.000000 20.000000 2.560000 18.00000
## [111,] 23.00000 25.000000 0.6666667 3.0000000 13.0000000 8.3333333 45.000000 19.333333 1.3333333 4.0000000 1.0000000 4.3333333 0.0000000 0.0000000 25.00000 14.800000 18.333333 2.013333 16.66667
## [112,] 17.00000 3.500000 0.0000000 0.0000000 0.0000000 3.5000000 28.500000 40.000000 6.0000000 3.0000000 0.0000000 17.0000000 2.0000000 0.0000000 20.00000 46.300000 17.500000 1.585000 35.00000
## [113,] 20.00000 7.500000 0.0000000 0.0000000 0.0000000 7.5000000 64.000000 21.500000 1.5000000 0.0000000 0.5000000 5.0000000 0.0000000 0.0000000 22.50000 26.400000 10.000000 1.110000 97.00000
## [114,] 21.66667 16.000000 0.0000000 1.3333333 0.0000000 14.6666667 37.333333 26.666667 3.6666667 2.6666667 0.0000000 13.6666667 0.0000000 0.0000000 20.00000 50.200000 21.666667 2.653333 63.33333
## [115,] 21.66667 16.000000 0.0000000 2.0000000 0.0000000 14.0000000 40.000000 25.666667 3.3333333 1.3333333 0.6666667 13.0000000 0.0000000 0.0000000 20.00000 44.533333 23.333333 2.983333 40.00000
## [116,] 26.33333 33.666667 4.0000000 3.6666667 0.0000000 26.0000000 40.333333 18.666667 3.6666667 1.0000000 0.3333333 2.3333333 0.0000000 0.0000000 25.00000 17.733333 26.666667 3.853333 16.00000
## [117,] 28.50000 34.500000 2.5000000 5.5000000 0.7500000 25.7500000 41.250000 13.750000 2.5000000 2.0000000 0.2500000 5.5000000 0.2500000 0.0000000 20.00000 48.525000 27.500000 4.480000 36.25000
## [118,] 33.25000 71.500000 2.2500000 7.5000000 51.2500000 10.5000000 19.250000 5.500000 2.1250000 1.6250000 0.0000000 0.0000000 0.0000000 0.0000000 26.25000 17.100000 26.250000 3.697500 20.75000
## [119,] 32.66667 61.000000 1.6666667 4.3333333 45.0000000 10.0000000 26.000000 6.666667 1.6666667 1.6666667 0.3333333 2.6666667 0.0000000 0.0000000 28.33333 19.566667 23.333333 3.073333 14.33333
## [120,] 24.33333 33.333333 2.3333333 9.0000000 0.0000000 22.0000000 37.000000 19.333333 1.3333333 2.5000000 0.1666667 6.3333333 0.0000000 0.0000000 25.00000 24.466667 25.000000 3.220000 16.00000
## [121,] 29.50000 39.000000 10.5000000 3.0000000 0.0000000 25.5000000 22.500000 12.500000 8.5000000 7.5000000 0.0000000 9.0000000 0.0000000 1.5000000 25.00000 12.000000 17.500000 1.865000 16.50000
## [122,] 27.50000 31.500000 1.5000000 17.5000000 0.0000000 12.5000000 33.000000 18.500000 4.5000000 5.5000000 0.0000000 5.5000000 0.0000000 1.5000000 25.00000 15.500000 20.000000 1.890000 15.00000
## [123,] 29.00000 39.000000 1.0000000 6.0000000 15.0000000 17.0000000 31.000000 18.500000 3.0000000 5.0000000 0.0000000 3.5000000 0.0000000 0.0000000 30.00000 24.250000 20.000000 1.800000 11.50000
## [124,] 32.00000 42.000000 0.0000000 3.0000000 28.0000000 11.0000000 34.000000 18.000000 2.0000000 4.0000000 0.0000000 0.0000000 0.0000000 0.0000000 25.00000 15.000000 20.000000 3.000000 18.00000
## [125,] 29.00000 43.500000 5.0000000 3.0000000 30.0000000 5.0000000 24.000000 16.000000 2.5000000 2.5000000 0.5000000 11.0000000 0.5000000 0.0000000 22.50000 29.000000 20.000000 2.355000 14.50000
## [126,] 32.25000 58.000000 6.2500000 2.5000000 37.2500000 9.7500000 26.000000 10.750000 2.5000000 4.2500000 0.7500000 0.0000000 0.0000000 0.2500000 28.75000 22.975000 26.250000 4.160000 21.25000
## [127,] 22.33333 5.000000 0.0000000 0.6666667 0.0000000 4.3333333 37.333333 30.000000 7.3333333 2.0000000 0.6666667 16.3333333 1.3333333 0.0000000 20.00000 54.233333 23.333333 3.600000 23.00000
## [128,] 18.00000 5.000000 0.0000000 0.0000000 0.0000000 5.0000000 43.500000 22.500000 6.5000000 4.0000000 2.5000000 17.5000000 0.0000000 0.0000000 22.50000 23.250000 20.000000 1.865000 19.50000
## [129,] 35.00000 79.000000 0.6666667 2.6666667 70.6666667 5.0000000 11.666667 5.000000 0.8333333 0.8333333 0.1666667 0.1666667 0.0000000 2.3333333 23.33333 28.300000 28.333333 5.533333 31.00000
## [130,] 29.33333 45.666667 0.0000000 2.6666667 36.0000000 7.0000000 36.333333 14.000000 1.1666667 0.8333333 0.1666667 0.1666667 0.0000000 1.6666667 26.66667 15.633333 18.333333 1.833333 43.33333
## [131,] 33.00000 58.500000 1.0000000 1.0000000 48.0000000 8.5000000 25.000000 10.000000 3.5000000 1.5000000 0.5000000 0.0000000 0.5000000 0.5000000 27.50000 19.100000 30.000000 5.580000 30.00000
## [132,] 26.00000 37.000000 4.0000000 9.0000000 6.0000000 18.0000000 33.000000 12.500000 3.5000000 6.5000000 0.0000000 7.5000000 0.0000000 0.0000000 27.50000 22.500000 22.500000 2.785000 20.00000
## [133,] 28.00000 35.000000 0.0000000 5.0000000 0.0000000 30.0000000 40.000000 15.000000 2.0000000 3.0000000 0.0000000 5.0000000 0.0000000 0.0000000 20.00000 34.000000 30.000000 5.770000 18.00000
## [134,] 32.00000 69.000000 0.0000000 9.0000000 50.0000000 10.0000000 20.000000 10.000000 0.5000000 0.5000000 0.0000000 0.0000000 0.0000000 0.0000000 20.00000 36.000000 30.000000 6.270000 21.00000
## [135,] 33.00000 54.000000 0.0000000 6.0000000 40.0000000 8.0000000 30.000000 12.000000 1.0000000 1.0000000 3.0000000 0.0000000 0.0000000 0.0000000 30.00000 27.000000 30.000000 5.430000 20.00000
## [136,] 25.33333 29.666667 1.6666667 2.3333333 16.6666667 9.0000000 41.666667 17.333333 2.0000000 2.6666667 0.0000000 6.6666667 0.0000000 0.0000000 11.66667 12.333333 21.666667 2.553333 16.00000
## [137,] 19.50000 0.000000 0.0000000 0.0000000 0.0000000 0.0000000 31.500000 42.500000 7.5000000 3.5000000 0.0000000 15.0000000 0.0000000 0.0000000 25.00000 48.500000 20.000000 1.855000 42.50000
## [138,] 26.50000 26.500000 2.5000000 3.0000000 2.5000000 18.5000000 35.500000 17.000000 7.5000000 5.5000000 1.5000000 5.0000000 1.5000000 0.0000000 17.50000 22.000000 20.000000 1.765000 21.50000
## [139,] 25.00000 14.000000 0.0000000 0.6666667 5.3333333 10.0000000 50.000000 17.000000 4.0000000 4.0000000 0.0000000 8.0000000 0.0000000 0.0000000 26.66667 20.666667 25.000000 3.520000 19.00000
## [140,] 17.00000 2.500000 0.0000000 0.0000000 0.0000000 2.5000000 32.500000 22.000000 7.0000000 6.0000000 0.0000000 30.0000000 0.0000000 0.0000000 22.50000 27.000000 12.500000 1.265000 25.00000
## [141,] 20.66667 3.333333 0.0000000 1.6666667 0.0000000 1.6666667 36.000000 38.333333 3.3333333 2.6666667 1.3333333 15.0000000 0.0000000 0.0000000 23.33333 47.333333 25.000000 3.676667 53.33333
## [142,] 19.75000 3.250000 0.0000000 0.1250000 0.0000000 3.1250000 18.750000 63.250000 8.2500000 2.7500000 0.0000000 3.7500000 0.0000000 0.0000000 27.50000 20.000000 20.000000 2.047500 36.25000
## [143,] 25.66667 36.333333 8.3333333 2.3333333 0.0000000 25.6666667 36.000000 18.000000 5.3333333 1.0000000 0.0000000 3.3333333 0.0000000 0.0000000 28.33333 21.666667 26.666667 3.663333 14.33333
## [144,] 27.00000 38.000000 3.3333333 5.0000000 1.3333333 26.6666667 35.000000 15.333333 4.0000000 2.0000000 2.0000000 7.6666667 0.0000000 0.0000000 20.00000 40.666667 23.333333 2.910000 23.33333
## [145,] 25.33333 60.000000 2.3333333 4.3333333 39.3333333 14.0000000 27.666667 8.666667 2.3333333 3.6666667 0.3333333 0.0000000 0.0000000 0.0000000 28.33333 24.700000 21.666667 2.756667 25.33333
## [146,] 23.75000 15.000000 0.7500000 0.7500000 0.0000000 13.5000000 44.500000 21.250000 5.7500000 4.7500000 0.7500000 8.0000000 0.0000000 0.0000000 20.00000 46.375000 21.250000 2.735000 28.75000
## [147,] 28.25000 47.750000 3.2500000 6.5000000 0.0000000 38.0000000 33.250000 10.000000 2.3750000 2.6250000 0.0000000 4.0000000 0.0000000 0.0000000 22.50000 35.000000 30.000000 6.555000 28.75000
## [148,] 29.80000 50.800000 10.2000000 3.2000000 7.4000000 30.0000000 27.000000 13.200000 4.4000000 2.0000000 0.0000000 2.6000000 0.0000000 0.0000000 24.00000 21.920000 24.000000 3.198000 18.20000
## [149,] 20.66667 15.333333 0.0000000 0.0000000 0.0000000 15.3333333 41.666667 26.666667 3.6666667 3.3333333 0.0000000 9.3333333 0.0000000 0.0000000 23.00000 41.333333 21.666667 2.426667 21.66667
## [150,] 27.00000 29.500000 0.6250000 3.8750000 0.0000000 25.0000000 38.625000 15.875000 5.1250000 3.2500000 0.0000000 7.3750000 0.2500000 0.0000000 25.62500 26.312500 21.250000 2.655000 21.12500
## [151,] 22.00000 11.666667 0.0000000 1.3333333 0.0000000 10.3333333 37.666667 37.666667 2.6666667 4.3333333 0.3333333 5.0000000 0.6666667 0.0000000 20.00000 47.266667 26.666667 3.636667 31.66667
## [152,] 23.33333 13.333333 0.0000000 0.3333333 0.0000000 13.0000000 41.000000 36.666667 1.0000000 2.6666667 0.0000000 4.6666667 0.6666667 0.0000000 28.33333 26.366667 23.333333 3.230000 21.66667
## [153,] 26.00000 35.000000 0.5000000 6.5000000 7.0000000 21.0000000 40.000000 17.500000 0.5000000 3.0000000 0.0000000 4.0000000 0.0000000 0.0000000 27.50000 18.450000 25.000000 3.215000 17.00000
## [154,] 33.50000 73.250000 0.0000000 1.0000000 70.5000000 1.7500000 16.500000 8.500000 0.7500000 0.5000000 0.2500000 0.2500000 0.0000000 0.0000000 27.50000 21.000000 25.000000 3.530000 40.00000
## [155,] 22.00000 13.666667 0.0000000 6.0000000 0.0000000 7.6666667 47.000000 20.333333 6.0000000 3.3333333 2.0000000 7.6666667 0.0000000 0.0000000 18.33333 13.100000 21.666667 2.226667 19.00000
## [156,] 22.00000 25.500000 0.0000000 12.0000000 0.0000000 13.5000000 29.000000 9.500000 1.0000000 7.0000000 0.5000000 27.5000000 0.0000000 0.0000000 15.00000 8.000000 25.000000 3.065000 18.00000
## [157,] 22.00000 15.000000 0.0000000 0.0000000 0.0000000 15.0000000 35.000000 30.000000 5.0000000 5.0000000 1.0000000 9.0000000 0.0000000 0.0000000 20.00000 43.000000 25.000000 3.300000 25.00000
## [158,] 17.00000 15.500000 2.5000000 3.5000000 0.0000000 9.5000000 40.000000 9.000000 7.5000000 4.5000000 1.0000000 22.5000000 0.0000000 0.0000000 15.00000 10.500000 15.000000 1.295000 45.00000
## [159,] 26.00000 37.500000 8.7500000 4.0000000 13.7500000 11.0000000 26.500000 14.000000 1.5000000 8.0000000 10.0000000 2.7500000 0.0000000 0.0000000 10.00000 6.250000 21.250000 2.437500 18.00000
## [160,] 23.50000 35.000000 8.0000000 7.5000000 3.0000000 16.5000000 32.500000 21.000000 3.0000000 4.0000000 0.0000000 4.5000000 0.0000000 0.0000000 25.00000 13.500000 20.000000 2.320000 19.00000
## [161,] 35.75000 80.750000 9.0000000 4.5000000 60.0000000 7.2500000 11.250000 4.750000 2.7500000 5.7500000 0.0000000 0.0000000 0.0000000 0.0000000 25.00000 23.000000 26.250000 3.922500 25.25000
## [162,] 24.80000 46.000000 4.0000000 4.6000000 26.0000000 11.4000000 17.000000 10.800000 11.2000000 3.0000000 0.2000000 12.0000000 0.0000000 0.0000000 20.00000 16.500000 22.000000 2.752000 14.20000
## [163,] 26.33333 57.000000 18.3333333 14.0000000 1.3333333 23.3333333 26.000000 9.666667 1.0000000 1.3333333 0.0000000 1.6666667 0.0000000 0.0000000 26.66667 22.000000 26.666667 3.746667 17.00000
## [164,] 30.33333 48.666667 2.6666667 19.0000000 0.0000000 27.0000000 24.666667 18.666667 3.3333333 1.6666667 0.0000000 3.0000000 0.0000000 0.0000000 26.66667 31.000000 20.000000 2.596667 13.66667
## [165,] 22.00000 33.333333 3.0000000 9.0000000 2.3333333 19.0000000 33.333333 18.333333 1.0000000 3.0000000 0.0000000 12.6666667 0.0000000 0.0000000 26.66667 20.333333 21.666667 2.656667 16.33333
## [166,] 31.25000 64.000000 10.7500000 16.0000000 3.5000000 29.2500000 23.250000 10.750000 1.2500000 2.2500000 0.2500000 2.7500000 0.0000000 0.0000000 26.25000 20.250000 30.000000 4.695000 16.50000
## [167,] 25.50000 31.500000 5.0000000 9.5000000 2.5000000 14.5000000 31.000000 17.500000 3.0000000 4.0000000 0.0000000 10.5000000 2.5000000 0.0000000 25.00000 11.500000 22.500000 2.745000 17.50000
## [168,] 31.33333 55.666667 10.6666667 4.3333333 14.0000000 26.6666667 33.666667 8.333333 1.3333333 3.3333333 0.0000000 0.3333333 0.0000000 0.0000000 26.66667 25.333333 26.666667 3.876667 16.00000
## [169,] 32.66667 64.000000 0.0000000 2.3333333 57.6666667 3.3333333 15.000000 6.666667 4.3333333 2.3333333 0.0000000 3.3333333 3.3333333 1.6666667 25.00000 26.333333 23.333333 3.820000 17.00000
## [170,] 23.00000 13.500000 0.0000000 3.0000000 2.5000000 8.0000000 42.500000 25.500000 3.5000000 4.0000000 0.5000000 10.5000000 0.0000000 0.0000000 22.50000 17.000000 17.500000 1.990000 13.00000
## [171,] 22.00000 24.000000 0.0000000 4.5000000 12.5000000 7.0000000 40.000000 30.000000 3.0000000 3.0000000 0.0000000 0.0000000 0.0000000 0.0000000 25.00000 27.000000 25.000000 3.465000 22.50000
## [172,] 20.50000 27.500000 3.0000000 3.5000000 11.5000000 9.5000000 27.500000 24.000000 6.0000000 5.0000000 0.0000000 10.0000000 0.0000000 0.0000000 27.50000 20.000000 20.000000 2.350000 16.50000
## [173,] 19.00000 15.500000 1.0000000 5.5000000 0.0000000 9.0000000 37.500000 22.000000 6.0000000 4.5000000 0.0000000 15.0000000 0.0000000 0.0000000 25.00000 12.500000 20.000000 1.995000 16.50000
## [174,] 28.00000 39.666667 3.6666667 6.6666667 14.6666667 14.6666667 37.000000 16.000000 3.3333333 2.6666667 0.0000000 1.3333333 0.0000000 0.0000000 21.66667 31.333333 23.333333 3.183333 20.33333
## [175,] 11.66667 1.666667 0.0000000 0.3333333 0.0000000 1.3333333 20.000000 8.666667 15.6666667 3.6666667 0.0000000 37.0000000 13.3333333 0.0000000 18.33333 17.100000 16.666667 1.950000 31.00000
## [176,] 23.00000 15.333333 0.0000000 2.0000000 0.0000000 13.3333333 36.666667 21.666667 7.6666667 2.6666667 1.0000000 13.3333333 0.0000000 1.6666667 23.33333 27.333333 18.333333 1.976667 38.66667
## [177,] 26.20000 29.750000 0.2000000 3.4000000 2.2000000 18.0000000 33.000000 16.000000 1.8000000 1.6000000 0.0000000 3.8000000 0.0000000 0.0000000 22.00000 45.200000 28.000000 4.720000 22.25000
## [178,] 29.42857 42.857143 4.0000000 10.5714286 3.8571429 24.4285714 30.142857 15.571429 3.4285714 1.8571429 0.1428571 6.1428571 0.0000000 0.0000000 23.57143 33.571429 26.428571 4.330000 19.71429
## [179,] 24.00000 34.000000 1.0000000 9.6666667 0.0000000 23.3333333 33.333333 23.666667 4.0000000 1.3333333 0.0000000 7.0000000 0.0000000 0.0000000 26.66667 31.000000 25.000000 3.486667 23.33333
## [180,] 24.50000 27.250000 0.0000000 5.5000000 0.0000000 21.7500000 35.500000 25.750000 3.2500000 4.7500000 0.0000000 3.5000000 0.0000000 0.0000000 23.75000 25.666667 27.500000 4.352500 18.33333
## [181,] 31.00000 52.000000 10.0000000 3.5000000 21.0000000 17.5000000 32.500000 7.500000 0.5000000 3.5000000 0.0000000 4.0000000 0.0000000 0.0000000 30.00000 25.500000 20.000000 2.250000 12.50000
## [182,] 23.25000 37.500000 2.2500000 12.2500000 2.2500000 20.7500000 39.000000 14.750000 4.5000000 0.3750000 0.1250000 1.5000000 0.2500000 2.0000000 20.00000 51.500000 15.000000 1.545000 98.75000
## [183,] 25.50000 32.000000 0.5000000 7.5000000 12.5000000 11.5000000 32.500000 11.500000 1.0000000 3.0000000 2.5000000 17.5000000 0.0000000 0.0000000 27.50000 25.500000 20.000000 1.530000 46.50000
## [184,] 30.66667 47.333333 0.3333333 19.3333333 4.3333333 23.3333333 35.000000 12.333333 2.0000000 1.6666667 0.0000000 1.6666667 0.0000000 0.0000000 23.33333 32.333333 25.000000 3.616667 20.33333
## [185,] 21.50000 22.500000 0.0000000 7.5000000 0.0000000 15.0000000 38.500000 14.500000 2.0000000 5.0000000 0.0000000 17.5000000 0.0000000 0.0000000 22.50000 15.750000 17.500000 1.590000 17.50000
## [186,] 19.50000 9.000000 0.0000000 3.0000000 0.0000000 6.0000000 32.500000 31.500000 3.0000000 5.0000000 1.5000000 17.5000000 0.0000000 0.0000000 27.50000 18.600000 20.000000 2.125000 18.50000
## [187,] 17.33333 2.500000 0.0000000 0.1666667 0.0000000 2.3333333 18.333333 17.000000 3.3333333 9.6666667 0.5000000 45.3333333 3.3333333 0.0000000 26.66667 19.100000 18.333333 1.600000 16.66667
## [188,] 25.33333 22.666667 0.0000000 1.0000000 13.3333333 8.3333333 34.000000 26.000000 4.6666667 2.3333333 0.6666667 9.6666667 0.0000000 0.0000000 23.33333 34.500000 23.333333 3.053333 23.33333
## [189,] 24.40000 27.800000 0.4000000 8.0000000 2.2000000 17.2000000 46.000000 19.800000 0.6000000 1.4000000 0.0000000 2.0000000 0.0000000 2.4000000 25.00000 29.880000 23.000000 3.344000 18.00000
## [190,] 26.60000 25.200000 2.4000000 2.0000000 1.6000000 19.2000000 46.600000 17.200000 1.0000000 2.4000000 0.2000000 7.4000000 0.0000000 0.0000000 22.00000 33.520000 22.000000 2.748000 32.00000
## [191,] 29.00000 42.333333 4.3333333 5.0000000 9.6666667 23.3333333 34.333333 13.333333 0.6666667 6.0000000 0.0000000 3.3333333 0.0000000 0.0000000 25.00000 30.933333 26.666667 3.626667 30.66667
## [192,] 34.00000 76.750000 1.5000000 2.0000000 66.2500000 7.0000000 16.750000 4.250000 0.7500000 1.5000000 0.0000000 0.0000000 0.0000000 0.0000000 23.75000 48.775000 28.250000 4.775000 32.50000rownames(phwhall_occobject$y)<-rownames(firstcheck_all)single year of surveying?
phwh_alltax_suff_env_detecthistory<-cbind(phwh_subset_suff_env_detecthistory,streambiotic_suff_biotic_bin)
phwh_alltax_suff_env_detecthistory$date<- as.Date(phwh_alltax_suff_env_detecthistory$date , format = "%Y/%m/%d")
phwh_alltax_suff_env_detecthistory$datecount<-as.numeric(phwh_alltax_suff_env_detecthistory$date-min(phwh_alltax_suff_env_detecthistory$date)+1)
phwh_alltax_suff_env_detecthistory$dayofyear<-format(phwh_alltax_suff_env_detecthistory$date, "%j")
phwh_alltax_suff_env_detecthistory_years<-phwh_alltax_suff_env_detecthistory
phwh_alltax_suff_env_detecthistory_years$year<-year(phwh_alltax_suff_env_detecthistory$date)
phwh_alltax_suff_env_detecthistory_years_2 <- phwh_alltax_suff_env_detecthistory_years %>%
group_by(stream_name,year,dayofyear)
summary(factor(phwh_alltax_suff_env_detecthistory_years$year))## 2003 2004 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
## 30 17 22 53 55 40 5 8 43 46 48 38 6 49 40 27 64phwh_alltax_suff_env_detecthistory_years_2008<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% 2008,] %>%
dplyr::select(-year)
phwh_alltax_suff_env_detecthistory_years_2008_firstcheck<- phwh_alltax_suff_env_detecthistory_years_2008 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[1]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
firstcheck_all_2008<-phwh_alltax_suff_env_detecthistory_years_2008_firstcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_secondcheck_2008<- phwh_alltax_suff_env_detecthistory_years_2008 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[2]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
secondcheck_all_2008<-phwh_alltax_suff_env_detecthistory_secondcheck_2008 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_thirdcheck_2008<- phwh_alltax_suff_env_detecthistory_years_2008 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[3]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
thirdcheck_all_2008<-phwh_alltax_suff_env_detecthistory_thirdcheck_2008 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
#stop at third survey - nothing further
sitechecks_2008<-phwh_alltax_suff_env_detecthistory_years_2008[,c(1,6)]
sitechecks_2008$date<- as.Date(sitechecks_2008$date , format = "%Y/%m/%d")
sitechecks_2008$dayofyear<-format(sitechecks_2008$date, "%j")
sitechecks_2008$datecount<-as.numeric(sitechecks_2008$date-min(sitechecks_2008$date)+1)
sitechecks_2008_surveyhistory<-sitechecks_2008 %>%
group_by(stream_name) %>%
mutate(check=as.character(dense_rank(date))) %>%
arrange(stream_name) %>%
distinct(stream_name, datecount, .keep_all = TRUE) %>%
spread(check,datecount) %>%
summarise(`1` = mean(`1`,na.rm=T),
`2`=mean(`2`,na.rm=T),
`3`=mean(`3`,na.rm=T),
)
phwhall_occobject_2008<-list()
phwhall_occobject_2008$y<-array(dim=c(108,36,3))
phwhall_occobject_2008$y <- array(c(as.matrix(firstcheck_all_2008),as.matrix(secondcheck_all_2008),
as.matrix(thirdcheck_all_2008)), dim=c(108,36,3))
phwhall_occobject_2008$det.covs$day<-as.matrix(sitechecks_2008_surveyhistory[,2:4])
dayyear_2008<-cbind(as.numeric(phwh_alltax_suff_env_detecthistory_years_2008_firstcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_secondcheck_2008$dayofyear),
as.numeric(phwh_alltax_suff_env_detecthistory_thirdcheck_2008$dayofyear))
phwhall_occobject_2008$det.covs$dayyear<-as.matrix(dayyear_2008)
phwh_subset_suff_env_detecthistory_2008<-phwh_alltax_suff_env_detecthistory_years_2008 %>%
dplyr::select(stream_name,reservation,latitude,longitude,river_basin,date,substrate_metric_total,percent_best_types,per_boulder_slabs,per_boulder_256mm,
per_bedrock,per_cobble,per_gravel,per_sand,per_silt,per_leaf_woody,per_detritus,per_clay_hardpan,per_muck,per_artificial,pool_metric_total,
pool_depth_.cm.,bankfull_metric_total,average_bankfull_.m.,sinuosity,X._canopy_open,temp,
dissolved_oxygen_mgl,ph,conductivity,hmfei_narrative) %>%
group_by(stream_name, reservation,date)
occcov_2008<-phwh_subset_suff_env_detecthistory_2008 %>% group_by(stream_name) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
occcov_2008_2<-occcov_2008[,c(-1,-4)]
cols<-seq(1,25,1)
occcov_2008_2[,cols] = apply(occcov_2008_2[,cols], 2, function(x) as.numeric(as.character(x)))
occcov_2008_3<-occcov_2008_2[ , colSums(is.na(occcov_2008_2)) == 0]
phwhall_occobject_2008$occ.covs<-as.matrix(occcov_2008_3)
library(rgdal)
d <- data.frame(lon=occcov_2008_3$longitude, lat=occcov_2008_3$latitude)
coordinates(d) <- c("lon", "lat")
proj4string(d) <- CRS("+init=epsg:4326") # WGS 84
CRS.new <- CRS("+init=epsg:3734")
d.3734 <- spTransform(d, CRS.new)
plot(d)
projcoords<-data.frame(d.3734)
phwhall_occobject_2008$coords<-as.matrix(projcoords)
rownames(phwhall_occobject_2008$y)<-rownames(firstcheck_all_2008)
phwh_alltax_suff_env_detecthistory_years_2009<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% 2009,]
phwh_alltax_suff_env_detecthistory_years_2014<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% 2014,]
phwh_alltax_suff_env_detecthistory_years_2015<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% 2015,]
phwh_alltax_suff_env_detecthistory_years_2018<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% 2018,]
occcov_4<-occcov_3
occcov_4$stream_name<-occcov$stream_name
write.csv(occcov_4,"PHWH_occcovs_mean.csv")#First cycle surveys - 2007-2010
phwh_alltax_suff_env_detecthistory_years_0710<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% c(2007,2008,2009,2010),] %>%
dplyr::select(-year)
phwh_alltax_suff_env_detecthistory_years_0710_firstcheck<- phwh_alltax_suff_env_detecthistory_years_0710 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[1]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
firstcheck_all_0710<-phwh_alltax_suff_env_detecthistory_years_0710_firstcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_secondcheck_0710<- phwh_alltax_suff_env_detecthistory_years_0710 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[2]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
secondcheck_all_0710<-phwh_alltax_suff_env_detecthistory_secondcheck_0710 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_thirdcheck_0710<- phwh_alltax_suff_env_detecthistory_years_0710 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[3]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
thirdcheck_all_0710<-phwh_alltax_suff_env_detecthistory_thirdcheck_0710 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_fourthcheck_0710<- phwh_alltax_suff_env_detecthistory_years_0710 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[4]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
fourthcheck_all_0710<-phwh_alltax_suff_env_detecthistory_fourthcheck_0710 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_fifthcheck_0710<- phwh_alltax_suff_env_detecthistory_years_0710 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[5]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
fifthcheck_all_0710<-phwh_alltax_suff_env_detecthistory_fifthcheck_0710 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
#stop at third survey - nothing further
sitechecks_0710<-phwh_alltax_suff_env_detecthistory_years_0710[,c(1,6)]
sitechecks_0710$date<- as.Date(sitechecks_0710$date , format = "%Y/%m/%d")
sitechecks_0710$dayofyear<-format(sitechecks_0710$date, "%j")
sitechecks_0710$datecount<-as.numeric(sitechecks_0710$date-min(sitechecks_0710$date)+1)
sitechecks_0710_surveyhistory<-sitechecks_0710 %>%
group_by(stream_name) %>%
mutate(check=as.character(dense_rank(date))) %>%
arrange(stream_name) %>%
distinct(stream_name, datecount, .keep_all = TRUE) %>%
spread(check,datecount) %>%
summarise(`1` = mean(`1`,na.rm=T),
`2`=mean(`2`,na.rm=T),
`3`=mean(`3`,na.rm=T),
)
phwhall_occobject_0710<-list()
phwhall_occobject_0710$y<-array(dim=c(108,141,3))
phwhall_occobject_0710$y <- array(c(as.matrix(firstcheck_all_0710),as.matrix(secondcheck_all_0710),
as.matrix(thirdcheck_all_0710)), dim=c(108,141,3))
phwhall_occobject_0710$det.covs$day<-as.matrix(sitechecks_0710_surveyhistory[,2:4])
dayyear_0710<-cbind(as.numeric(phwh_alltax_suff_env_detecthistory_years_0710_firstcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_secondcheck_0710$dayofyear),
as.numeric(phwh_alltax_suff_env_detecthistory_thirdcheck_0710$dayofyear))
phwhall_occobject_0710$det.covs$dayyear<-as.matrix(dayyear_0710)
phwh_subset_suff_env_detecthistory_0710<-phwh_alltax_suff_env_detecthistory_years_0710 %>%
dplyr::select(stream_name,reservation,latitude,longitude,river_basin,date,substrate_metric_total,percent_best_types,per_boulder_slabs,per_boulder_256mm,
per_bedrock,per_cobble,per_gravel,per_sand,per_silt,per_leaf_woody,per_detritus,per_clay_hardpan,per_muck,per_artificial,pool_metric_total,
pool_depth_.cm.,bankfull_metric_total,average_bankfull_.m.,sinuosity,X._canopy_open,temp,
dissolved_oxygen_mgl,ph,conductivity,hmfei_narrative) %>%
group_by(stream_name, reservation,date)
occcov_0710<-phwh_subset_suff_env_detecthistory_0710 %>% group_by(stream_name) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
occcov_0710_2<-occcov_0710[,c(-1,-4)]
cols<-seq(1,25,1)
occcov_0710_2[,cols] = apply(occcov_0710_2[,cols], 2, function(x) as.numeric(as.character(x)))
occcov_0710_3<-occcov_0710_2[ , colSums(is.na(occcov_0710_2)) == 0]
phwhall_occobject_0710$occ.covs<-as.matrix(occcov_0710_3)
library(rgdal)
d <- data.frame(lon=occcov_0710_3$longitude, lat=occcov_0710_3$latitude)
coordinates(d) <- c("lon", "lat")
proj4string(d) <- CRS("+init=epsg:4326") # WGS 84
CRS.new <- CRS("+init=epsg:3734")
d.3734 <- spTransform(d, CRS.new)
plot(d)
projcoords<-data.frame(d.3734)
phwhall_occobject_0710$coords<-as.matrix(projcoords)
rownames(phwhall_occobject_0710$y)<-rownames(firstcheck_all_0710)#First cycle surveys - 2013-2016
phwh_alltax_suff_env_detecthistory_years_1316<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% c(2013,2014,2015,2016),] %>%
dplyr::select(-year)
phwh_alltax_suff_env_detecthistory_years_1316_firstcheck<- phwh_alltax_suff_env_detecthistory_years_1316 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[1]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
firstcheck_all_1316<-phwh_alltax_suff_env_detecthistory_years_1316_firstcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_secondcheck_1316<- phwh_alltax_suff_env_detecthistory_years_1316 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[2]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
secondcheck_all_1316<-phwh_alltax_suff_env_detecthistory_secondcheck_1316 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_thirdcheck_1316<- phwh_alltax_suff_env_detecthistory_years_1316 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[3]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
thirdcheck_all_1316<-phwh_alltax_suff_env_detecthistory_thirdcheck_1316 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_fourthcheck_1316<- phwh_alltax_suff_env_detecthistory_years_1316 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[4]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
fourthcheck_all_1316<-phwh_alltax_suff_env_detecthistory_fourthcheck_1316 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_fifthcheck_1316<- phwh_alltax_suff_env_detecthistory_years_1316 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[5]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
fifthcheck_all_1316<-phwh_alltax_suff_env_detecthistory_fifthcheck_1316 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
#stop at third survey - not much further
sitechecks_1316<-phwh_alltax_suff_env_detecthistory_years_1316[,c(1,6)]
sitechecks_1316$date<- as.Date(sitechecks_1316$date , format = "%Y/%m/%d")
sitechecks_1316$dayofyear<-format(sitechecks_1316$date, "%j")
sitechecks_1316$datecount<-as.numeric(sitechecks_1316$date-min(sitechecks_1316$date)+1)
sitechecks_1316_surveyhistory<-sitechecks_1316 %>%
group_by(stream_name) %>%
mutate(check=as.character(dense_rank(date))) %>%
arrange(stream_name) %>%
distinct(stream_name, datecount, .keep_all = TRUE) %>%
spread(check,datecount) %>%
summarise(`1` = mean(`1`,na.rm=T),
`2`=mean(`2`,na.rm=T),
`3`=mean(`3`,na.rm=T),
)
phwhall_occobject_1316<-list()
phwhall_occobject_1316$y<-array(dim=c(108,154,3))
phwhall_occobject_1316$y <- array(c(as.matrix(firstcheck_all_1316),as.matrix(secondcheck_all_1316),
as.matrix(thirdcheck_all_1316)), dim=c(108,154,3))
phwhall_occobject_1316$det.covs$day<-as.matrix(sitechecks_1316_surveyhistory[,2:4])
dayyear_1316<-cbind(as.numeric(phwh_alltax_suff_env_detecthistory_years_1316_firstcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_secondcheck_1316$dayofyear),
as.numeric(phwh_alltax_suff_env_detecthistory_thirdcheck_1316$dayofyear))
phwhall_occobject_1316$det.covs$dayyear<-as.matrix(dayyear_1316)
phwh_subset_suff_env_detecthistory_1316<-phwh_alltax_suff_env_detecthistory_years_1316 %>%
dplyr::select(stream_name,reservation,latitude,longitude,river_basin,date,substrate_metric_total,percent_best_types,per_boulder_slabs,per_boulder_256mm,
per_bedrock,per_cobble,per_gravel,per_sand,per_silt,per_leaf_woody,per_detritus,per_clay_hardpan,per_muck,per_artificial,pool_metric_total,
pool_depth_.cm.,bankfull_metric_total,average_bankfull_.m.,sinuosity,X._canopy_open,temp,
dissolved_oxygen_mgl,ph,conductivity,hmfei_narrative) %>%
group_by(stream_name, reservation,date)
occcov_1316<-phwh_subset_suff_env_detecthistory_1316 %>% group_by(stream_name) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
occcov_1316_2<-occcov_1316[,c(-1,-4)]
cols<-seq(1,25,1)
occcov_1316_2[,cols] = apply(occcov_1316_2[,cols], 2, function(x) as.numeric(as.character(x)))
occcov_1316_3<-occcov_1316_2[ , colSums(is.na(occcov_1316_2)) == 0]
phwhall_occobject_1316$occ.covs<-as.matrix(occcov_1316_3)
library(rgdal)
d <- data.frame(lon=occcov_1316_3$longitude, lat=occcov_1316_3$latitude)
coordinates(d) <- c("lon", "lat")
proj4string(d) <- CRS("+init=epsg:4326") # WGS 84
CRS.new <- CRS("+init=epsg:3734")
d.3734 <- spTransform(d, CRS.new)
plot(d)
projcoords<-data.frame(d.3734)
phwhall_occobject_1316$coords<-as.matrix(projcoords)
rownames(phwhall_occobject_1316$y)<-rownames(firstcheck_all_1316)#First cycle surveys - 2017-2021
phwh_alltax_suff_env_detecthistory_years_1821<-phwh_alltax_suff_env_detecthistory_years[phwh_alltax_suff_env_detecthistory_years$year %in% c(2018,2019,2020,2021),] %>%
dplyr::select(-year)
phwh_alltax_suff_env_detecthistory_years_1821_firstcheck<- phwh_alltax_suff_env_detecthistory_years_1821 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[1]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude, date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
firstcheck_all_1821<-phwh_alltax_suff_env_detecthistory_years_1821_firstcheck %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
phwh_alltax_suff_env_detecthistory_secondcheck_1821<- phwh_alltax_suff_env_detecthistory_years_1821 %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[2]))) %>%
unnest(cols = c(reservation, river_basin, latitude,longitude,date, substrate_metric_total, percent_best_types,
per_boulder_slabs, per_boulder_256mm, per_bedrock, per_cobble,
per_gravel, per_sand, per_silt, per_leaf_woody, per_detritus,
per_clay_hardpan, per_muck, per_artificial, pool_metric_total,
pool_depth_.cm., bankfull_metric_total, average_bankfull_.m.,
sinuosity, X._canopy_open, temp, dissolved_oxygen_mgl, ph,
conductivity, hmfei_narrative, streambiotic_suff_mtdusky,
streambiotic_suff_nodusky, streambiotic_suff_twolined, streambiotic_suff_longtail,
streambiotic_suff_red, streambiotic_suff_mole, streambiotic_suff_fourtoed,
streambiotic_suff_redback, streambiotic_suff_slimy, blacknose_dace,
bluegill_sunfish, bluntnose_minnow, brindled_madtom, brook_stickleback,
brook_trout, central_mudminnow, central_stoneroller_minnow,
common_shiner, creek_chub, eastern_sand_darter, fantail_darter,
fathead_minnow, golden_shiner, goldfish, grass_pickerel,
greenside_darter, green_sunfish, johnny_darter, largemouth_bass,
longear_sunfish, longnose_dace, mottled_sculpin, northern_hog_sucker,
pumpkinseed_sunfish, rainbow_darter, rainbow_trout, redbelly_dace,
redside_dace, river_chub, silverjaw_minnow, smallmouth_bass,
spotfin_shiner, stonecat_madtom, striped_shiner, trout_perch,
warmouth_sunfish, white_sucker, yellow_bullhead_catfish,
sessile_animals, aquatic_worms, sow_bugs, scuds, water_mites,
damselfly_nymphs, alderfly_larvae, other_beetles, crayfish,
dragonfly_nymphs, riffle_beetles, other_flies, midges, snails,
clams, fishfly_larvae, water_penny_beetles, cranefly_larvae,
ameletidae, arthropleidae, baetidae, baetiscidae, caenidae,
ephemerellidae, ephemeridae, heptageniidae, isonychiidae,
leptohyphidae, leptophlebiidae, polymitarcyidae, potamanthidae,
pseudironidae, siphlonuridae, capniidae, chloroperlidae,
leuctridae, nemouridae, peltoperlidae, perlidae, perlodidae,
pteronarcyidae, taeniopterygidae, brachycentridae, dipseuodopsidae,
glossosomatidae, goeridae, helicopsychidae, hydropsychidae,
hydroptilidae, lepidostomatidae, leptoceridae, limnephilidae,
molannidae, odontoceridae, philopotamidae, phryganeidae,
polycentropodidae, psychomiidae, rhyacophilidae, uenoidae,
datecount, dayofyear))
secondcheck_all_1821<-phwh_alltax_suff_env_detecthistory_secondcheck_1821 %>%
dplyr::select(streambiotic_suff_mtdusky:uenoidae) %>%
rownames_to_column() %>%
pivot_longer(-rowname) %>%
pivot_wider(names_from=rowname) %>%
column_to_rownames(var = "name")
#stop at second survey - not much further
sitechecks_1821<-phwh_alltax_suff_env_detecthistory_years_1821[,c(1,6)]
sitechecks_1821$date<- as.Date(sitechecks_1821$date , format = "%Y/%m/%d")
sitechecks_1821$dayofyear<-format(sitechecks_1821$date, "%j")
sitechecks_1821$datecount<-as.numeric(sitechecks_1821$date-min(sitechecks_1821$date)+1)
sitechecks_1821_surveyhistory<-sitechecks_1821 %>%
group_by(stream_name) %>%
mutate(check=as.character(dense_rank(date))) %>%
arrange(stream_name) %>%
distinct(stream_name, datecount, .keep_all = TRUE) %>%
spread(check,datecount) %>%
summarise(`1` = mean(`1`,na.rm=T),
`2`=mean(`2`,na.rm=T)
)
phwhall_occobject_1821<-list()
phwhall_occobject_1821$y<-array(dim=c(108,171,2))
phwhall_occobject_1821$y <- array(c(as.matrix(firstcheck_all_1821),as.matrix(secondcheck_all_1821)), dim=c(108,171,2))
phwhall_occobject_1821$det.covs$day<-as.matrix(sitechecks_1821_surveyhistory[,2:3])
dayyear_1821<-cbind(as.numeric(phwh_alltax_suff_env_detecthistory_years_1821_firstcheck$dayofyear),as.numeric(phwh_alltax_suff_env_detecthistory_secondcheck_1821$dayofyear))
phwhall_occobject_1821$det.covs$dayyear<-as.matrix(dayyear_1821)
phwh_subset_suff_env_detecthistory_1821<-phwh_alltax_suff_env_detecthistory_years_1821 %>%
dplyr::select(stream_name,reservation,latitude,longitude,river_basin,date,substrate_metric_total,percent_best_types,per_boulder_slabs,per_boulder_256mm,
per_bedrock,per_cobble,per_gravel,per_sand,per_silt,per_leaf_woody,per_detritus,per_clay_hardpan,per_muck,per_artificial,pool_metric_total,
pool_depth_.cm.,bankfull_metric_total,average_bankfull_.m.,sinuosity,X._canopy_open,temp,
dissolved_oxygen_mgl,ph,conductivity,hmfei_narrative) %>%
group_by(stream_name, reservation,date)
occcov_1821<-phwh_subset_suff_env_detecthistory_1821 %>% group_by(stream_name) %>%
summarise_if(is.numeric, mean, na.rm = TRUE)
occcov_1821_2<-occcov_1821[,c(-1,-4)]
cols<-seq(1,25,1)
occcov_1821_2[,cols] = apply(occcov_1821_2[,cols], 2, function(x) as.numeric(as.character(x)))
occcov_1821_3<-occcov_1821_2[ , colSums(is.na(occcov_1821_2)) == 0]
phwhall_occobject_1821$occ.covs<-as.matrix(occcov_1821_3)
library(rgdal)
d <- data.frame(lon=occcov_1821_3$longitude, lat=occcov_1821_3$latitude)
coordinates(d) <- c("lon", "lat")
proj4string(d) <- CRS("+init=epsg:4326") # WGS 84
CRS.new <- CRS("+init=epsg:3734")
d.3734 <- spTransform(d, CRS.new)
plot(d)
projcoords<-data.frame(d.3734)
phwhall_occobject_1821$coords<-as.matrix(projcoords)
rownames(phwhall_occobject_1821$y)<-rownames(firstcheck_all_1821)RDAs
library(vegan)
p_alltax_step_0<-rda(streambioticsum_suff_biotic_bin~1,occcov_3[,-1],scale=T)
p_alltax_step_1<-rda(streambioticsum_suff_biotic_bin~.,occcov_3[,-1],scale=T)
set.seed(12345)
p_alltax_step<- ordistep(p_alltax_step_0, scope = formula(p_alltax_step_1))##
## Start: streambioticsum_suff_biotic_bin ~ 1
##
## Df AIC F Pr(>F)
## + pool_depth_.cm. 1 802.90 4.5215 0.005 **
## + bankfull_metric_total 1 803.39 4.0226 0.005 **
## + average_bankfull_.m. 1 803.42 3.9945 0.005 **
## + per_sand 1 803.96 3.4480 0.005 **
## + percent_best_types 1 804.18 3.2291 0.005 **
## + per_cobble 1 804.21 3.1975 0.005 **
## + per_clay_hardpan 1 804.48 2.9274 0.005 **
## + pool_metric_total 1 804.89 2.5114 0.005 **
## + per_bedrock 1 805.21 2.1893 0.005 **
## + per_detritus 1 804.89 2.5072 0.010 **
## + X._canopy_open 1 805.00 2.3983 0.010 **
## + per_boulder_256mm 1 805.32 2.0847 0.015 *
## + per_gravel 1 805.50 1.8972 0.020 *
## + per_boulder_slabs 1 805.66 1.7380 0.020 *
## + per_leaf_woody 1 804.72 2.6799 0.035 *
## + per_silt 1 805.90 1.4983 0.120
## + per_muck 1 806.22 1.1828 0.230
## + per_artificial 1 806.33 1.0733 0.270
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm.
##
## Df AIC F Pr(>F)
## - pool_depth_.cm. 1 805.41 4.5215 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + per_sand 1 801.09 3.7809 0.005 **
## + percent_best_types 1 801.53 3.3386 0.005 **
## + per_cobble 1 801.86 3.0134 0.005 **
## + per_clay_hardpan 1 802.17 2.7046 0.005 **
## + average_bankfull_.m. 1 802.26 2.6126 0.005 **
## + bankfull_metric_total 1 802.26 2.6096 0.005 **
## + per_bedrock 1 802.47 2.4053 0.005 **
## + pool_metric_total 1 802.73 2.1430 0.010 **
## + per_boulder_256mm 1 803.07 1.8085 0.020 *
## + per_detritus 1 802.88 1.9947 0.035 *
## + per_boulder_slabs 1 803.07 1.8075 0.045 *
## + per_leaf_woody 1 802.54 2.3336 0.055 .
## + X._canopy_open 1 803.33 1.5479 0.075 .
## + per_silt 1 803.29 1.5903 0.105
## + per_muck 1 803.55 1.3271 0.165
## + per_artificial 1 803.88 0.9982 0.375
## + per_gravel 1 803.91 0.9709 0.500
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand
##
## Df AIC F Pr(>F)
## - per_sand 1 802.90 3.7809 0.005 **
## - pool_depth_.cm. 1 803.96 4.8506 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + per_cobble 1 799.42 3.6300 0.005 **
## + percent_best_types 1 799.69 3.3648 0.005 **
## + per_boulder_256mm 1 800.81 2.2442 0.005 **
## + per_clay_hardpan 1 800.53 2.5259 0.010 **
## + per_leaf_woody 1 800.70 2.3572 0.020 *
## + pool_metric_total 1 801.11 1.9516 0.025 *
## + per_boulder_slabs 1 801.30 1.7674 0.035 *
## + bankfull_metric_total 1 801.55 1.5150 0.035 *
## + average_bankfull_.m. 1 801.58 1.4917 0.045 *
## + per_gravel 1 801.67 1.3991 0.050 *
## + per_detritus 1 801.28 1.7823 0.055 .
## + X._canopy_open 1 801.50 1.5649 0.085 .
## + per_silt 1 801.45 1.6167 0.090 .
## + per_muck 1 801.80 1.2690 0.205
## + per_bedrock 1 801.96 1.1126 0.260
## + per_artificial 1 802.20 0.8715 0.430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble
##
## Df AIC F Pr(>F)
## - per_cobble 1 801.09 3.6300 0.005 **
## - per_sand 1 801.86 4.3959 0.005 **
## - pool_depth_.cm. 1 802.48 5.0177 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + percent_best_types 1 799.05 2.3189 0.005 **
## + per_gravel 1 799.28 2.0932 0.005 **
## + per_bedrock 1 799.25 2.1252 0.010 **
## + pool_metric_total 1 799.46 1.9175 0.015 *
## + per_boulder_256mm 1 799.70 1.6818 0.020 *
## + per_clay_hardpan 1 799.62 1.7635 0.035 *
## + bankfull_metric_total 1 799.98 1.4126 0.045 *
## + per_detritus 1 799.42 1.9620 0.055 .
## + per_leaf_woody 1 799.49 1.8927 0.085 .
## + X._canopy_open 1 799.92 1.4691 0.095 .
## + average_bankfull_.m. 1 800.07 1.3171 0.120
## + per_silt 1 799.94 1.4520 0.140
## + per_muck 1 800.49 0.9114 0.480
## + per_artificial 1 800.63 0.7713 0.590
## + per_boulder_slabs 1 800.56 0.8403 0.655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types
##
## Df AIC F Pr(>F)
## - percent_best_types 1 799.42 2.3189 0.005 **
## - per_cobble 1 799.69 2.5812 0.005 **
## - per_sand 1 801.34 4.2206 0.005 **
## - pool_depth_.cm. 1 802.07 4.9446 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + pool_metric_total 1 799.01 1.9918 0.005 **
## + per_gravel 1 799.03 1.9698 0.005 **
## + bankfull_metric_total 1 799.31 1.6932 0.020 *
## + average_bankfull_.m. 1 799.52 1.4922 0.025 *
## + per_boulder_256mm 1 799.56 1.4572 0.040 *
## + per_detritus 1 798.75 2.2431 0.050 *
## + per_clay_hardpan 1 799.55 1.4652 0.080 .
## + per_leaf_woody 1 799.58 1.4369 0.125
## + X._canopy_open 1 799.56 1.4493 0.140
## + per_silt 1 799.75 1.2635 0.145
## + per_bedrock 1 799.87 1.1533 0.230
## + per_muck 1 800.20 0.8287 0.595
## + per_artificial 1 800.31 0.7234 0.655
## + per_boulder_slabs 1 800.20 0.8273 0.765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + pool_metric_total
##
## Df AIC F Pr(>F)
## - pool_metric_total 1 799.05 1.9918 0.010 **
## - percent_best_types 1 799.46 2.3913 0.005 **
## - per_cobble 1 799.69 2.6133 0.005 **
## - per_sand 1 801.47 4.3697 0.005 **
## - pool_depth_.cm. 1 801.62 4.5187 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + per_gravel 1 799.36 1.5918 0.020 *
## + bankfull_metric_total 1 799.30 1.6499 0.030 *
## + per_detritus 1 799.09 1.8610 0.040 *
## + average_bankfull_.m. 1 799.46 1.5008 0.040 *
## + per_boulder_256mm 1 799.49 1.4678 0.055 .
## + X._canopy_open 1 799.50 1.4606 0.090 .
## + per_silt 1 799.70 1.2654 0.165
## + per_clay_hardpan 1 799.69 1.2726 0.185
## + per_leaf_woody 1 799.84 1.1275 0.205
## + per_bedrock 1 799.82 1.1530 0.220
## + per_muck 1 800.14 0.8375 0.585
## + per_artificial 1 800.26 0.7267 0.685
## + per_boulder_slabs 1 800.13 0.8447 0.715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + pool_metric_total + per_gravel
##
## Df AIC F Pr(>F)
## - pool_metric_total 1 799.03 1.6137 0.045 *
## - per_gravel 1 799.01 1.5918 0.025 *
## - percent_best_types 1 799.47 2.0395 0.005 **
## - per_cobble 1 800.55 3.0975 0.005 **
## - pool_depth_.cm. 1 801.24 3.7754 0.005 **
## - per_sand 1 801.87 4.3891 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + per_detritus 1 799.49 1.8097 0.040 *
## + per_boulder_256mm 1 799.85 1.4568 0.040 *
## + average_bankfull_.m. 1 799.94 1.3712 0.075 .
## + bankfull_metric_total 1 799.94 1.3731 0.080 .
## + per_silt 1 800.01 1.2985 0.125
## + X._canopy_open 1 799.86 1.4509 0.145
## + per_leaf_woody 1 800.04 1.2770 0.185
## + per_bedrock 1 800.14 1.1760 0.270
## + per_muck 1 800.43 0.9020 0.505
## + per_clay_hardpan 1 800.46 0.8665 0.550
## + per_boulder_slabs 1 800.45 0.8783 0.620
## + per_artificial 1 800.59 0.7388 0.625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + pool_metric_total + per_gravel + per_detritus
##
## Df AIC F Pr(>F)
## - pool_metric_total 1 798.90 1.3593 0.120
## - per_detritus 1 799.36 1.8097 0.075 .
## - per_gravel 1 799.09 1.5420 0.045 *
## - percent_best_types 1 799.74 2.1778 0.005 **
## - per_cobble 1 800.75 3.1549 0.005 **
## - pool_depth_.cm. 1 801.16 3.5604 0.005 **
## - per_sand 1 802.05 4.4257 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + per_gravel + per_detritus
##
## Df AIC F Pr(>F)
## + average_bankfull_.m. 1 799.47 1.3693 0.065 .
## + bankfull_metric_total 1 799.50 1.3497 0.075 .
## + per_boulder_256mm 1 799.42 1.4209 0.085 .
## + pool_metric_total 1 799.49 1.3593 0.085 .
## + per_leaf_woody 1 799.40 1.4429 0.090 .
## + per_silt 1 799.54 1.3035 0.110
## + X._canopy_open 1 799.38 1.4610 0.125
## + per_bedrock 1 799.69 1.1603 0.205
## + per_boulder_slabs 1 799.88 0.9771 0.455
## + per_muck 1 799.93 0.9346 0.490
## + per_artificial 1 800.00 0.8632 0.490
## + per_clay_hardpan 1 799.98 0.8825 0.520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## - per_detritus 1 799.03 2.0671 0.050 *
## - per_gravel 1 798.75 1.7955 0.005 **
## - percent_best_types 1 799.40 2.4251 0.005 **
## - per_cobble 1 800.16 3.1717 0.005 **
## - pool_depth_.cm. 1 800.59 3.5907 0.005 **
## - per_sand 1 801.46 4.4482 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + per_boulder_256mm 1 799.42 1.4209 0.050 *
## + average_bankfull_.m. 1 799.47 1.3693 0.055 .
## + pool_metric_total 1 799.49 1.3593 0.095 .
## + X._canopy_open 1 799.38 1.4610 0.115
## + bankfull_metric_total 1 799.50 1.3497 0.120
## + per_silt 1 799.54 1.3035 0.140
## + per_leaf_woody 1 799.40 1.4429 0.155
## + per_bedrock 1 799.69 1.1603 0.235
## + per_boulder_slabs 1 799.88 0.9771 0.420
## + per_artificial 1 800.00 0.8632 0.465
## + per_muck 1 799.93 0.9346 0.485
## + per_clay_hardpan 1 799.98 0.8825 0.525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Step: streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + per_gravel + per_detritus + per_boulder_256mm
##
## Df AIC F Pr(>F)
## - per_boulder_256mm 1 798.90 1.4209 0.055 .
## - per_detritus 1 799.56 2.0625 0.050 *
## - per_gravel 1 799.27 1.7755 0.015 *
## - percent_best_types 1 799.75 2.2417 0.005 **
## - per_cobble 1 800.63 3.0992 0.005 **
## - pool_depth_.cm. 1 800.69 3.1551 0.005 **
## - per_sand 1 801.79 4.2366 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Df AIC F Pr(>F)
## + average_bankfull_.m. 1 800.03 1.3263 0.070 .
## + pool_metric_total 1 799.97 1.3866 0.090 .
## + X._canopy_open 1 799.90 1.4569 0.115
## + bankfull_metric_total 1 800.04 1.3234 0.125
## + per_leaf_woody 1 799.86 1.4939 0.145
## + per_silt 1 800.07 1.2893 0.165
## + per_artificial 1 800.45 0.9242 0.440
## + per_muck 1 800.43 0.9465 0.465
## + per_clay_hardpan 1 800.50 0.8817 0.470
## + per_boulder_slabs 1 800.42 0.9557 0.485
## + per_bedrock 1 800.49 0.8919 0.690
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1p_alltax_step## Call: rda(formula = streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + per_gravel + per_detritus + per_boulder_256mm, data = occcov_3[, -1], scale = T)
##
## Inertia Proportion Rank
## Total 66.00000 1.00000
## Constrained 6.52586 0.09888 7
## Unconstrained 59.47414 0.90112 66
## Inertia is correlations
##
## Eigenvalues for constrained axes:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7
## 2.6830 1.5345 1.0334 0.5284 0.3645 0.2091 0.1730
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 7.290 3.042 2.498 2.315 1.980 1.889 1.765 1.653
## (Showing 8 of 66 unconstrained eigenvalues)anova(p_alltax_step)## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + per_gravel + per_detritus + per_boulder_256mm, data = occcov_3[, -1], scale = T)
## Df Variance F Pr(>F)
## Model 7 6.526 2.8842 0.001 ***
## Residual 184 59.474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(p_alltax_step,by="term")## Permutation test for rda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = streambioticsum_suff_biotic_bin ~ pool_depth_.cm. + per_sand + per_cobble + percent_best_types + per_gravel + per_detritus + per_boulder_256mm, data = occcov_3[, -1], scale = T)
## Df Variance F Pr(>F)
## pool_depth_.cm. 1 1.534 4.7462 0.001 ***
## per_sand 1 1.264 3.9115 0.001 ***
## per_cobble 1 1.197 3.7039 0.001 ***
## percent_best_types 1 0.759 2.3497 0.003 **
## per_gravel 1 0.642 1.9856 0.006 **
## per_detritus 1 0.670 2.0718 0.039 *
## per_boulder_256mm 1 0.459 1.4209 0.064 .
## Residual 184 59.474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(ggord)
phwh_salamander_suff_sum_bin<-phwh_salamander_suff_sum %>% mutate_if(is.numeric, ~1 * (. >= 1))
phwh_salamander_suff_env_detecthistory<-cbind(phwh_subset_suff_env_detecthistory,phwh_salamander_suff_sum_bin[,-1:-2])
phwh_salamander_suff_env_detecthistory$datecount<-sitechecks$datecount
phwh_salamander_suff_env_detecthistory$dayofyear<-sitechecks$dayofyear
phwh_salamander_suff_env_detecthistory<-phwh_salamander_suff_env_detecthistory %>%
dplyr::select(!c(latitude,longitude))
phwh_subset_suff_env_detecthistory_first<- phwh_salamander_suff_env_detecthistory %>% group_by(stream_name) %>%
arrange(datecount) %>%
summarise_all(funs(list(.[1]))) %>%
unnest(cols = c(date, reservation, river_basin,hmfei_narrative, substrate_metric_total, percent_best_types, per_boulder_slabs,
per_boulder_256mm, per_bedrock, per_cobble, per_gravel, per_sand,
per_silt, per_leaf_woody, per_detritus, per_clay_hardpan,
per_muck, per_artificial, pool_metric_total, pool_depth_.cm.,
bankfull_metric_total, average_bankfull_.m., sinuosity, X._canopy_open,
temp, dissolved_oxygen_mgl, ph, conductivity, mtdusky, nodusky,
twolined, longtail, red, mole, fourtoed, redback, slimy,
datecount, dayofyear))
hmfei_narrative<-phwh_subset_suff_env_detecthistory_first$hmfei_narrative
ggord(p_alltax_step, xlim=c(-1,1.25),ylim = c(-1.25, 0.75),txt=4,arrow=TRUE,ptslab=TRUE,addsize=3, size =4,grp_in=hmfei_narrative)
This sets up a fairly clear contrast between substrate sandiness and most other substrate metrics.
taxa most differentiated with axes
## most differentiated taxa
plot_df <- scores(p_alltax_step, display = "sites") %>%
as.data.frame()
plot_df$stream_name<-streambioticsum_suff$stream_name
plot_df <- plot_df %>%
full_join(streambioticsum_suff, by = "stream_name")
# envfit() takes the output of metaMDS() and the species matrix you created
fitrdaall <- envfit(p_alltax_step, streambioticsum_suff_biotic_bin, perm = 999)
# extract p-values for each species
fitrdaall_pvals <- fitrdaall$vectors$pvals %>%
as.data.frame() %>%
rownames_to_column("species") %>%
dplyr::rename("pvals" = ".")
# extract coordinates for species, only keep species with p-val = 0.001
fit_spp <- fitrdaall %>%
scores(., display = "vectors") %>%
as.data.frame() %>%
rownames_to_column("species") %>%
full_join(., fitrdaall_pvals, by = "species") %>%
filter(pvals == 0.001)
rdaall_plot_new <- ggplot(plot_df, aes(x = RDA1, y = RDA2)) +
coord_fixed() +
geom_point(aes(color = hmfei_narrative), size = 3, alpha = 0.8) +
stat_ellipse(aes(color = hmfei_narrative)) +
geom_segment(data = fit_spp, aes(x = 0, xend = RDA1, y = 0, yend = RDA2),
col = "black") +
geom_text(data = fit_spp, aes(label = species))
rdaall_plot_new
fit_spp## species RDA1 RDA2 pvals
## 1 streambioticsum_suff_nodusky -0.329216674 0.155108131 0.001
## 2 streambioticsum_suff_twolined -0.285661542 0.556360216 0.001
## 3 streambioticsum_suff_longtail -0.336955690 -0.120614444 0.001
## 4 streambioticsum_suff_red -0.723512943 -0.055929464 0.001
## 5 streambioticsum_suff_redback -0.537499021 -0.006655245 0.001
## 6 streambioticsum_suff_slimy -0.278494096 0.197728949 0.001
## 7 blacknose_dace 0.155599595 0.420050709 0.001
## 8 creek_chub 0.226821069 0.392966202 0.001
## 9 aquatic_worms 0.192709566 0.288216489 0.001
## 10 sow_bugs 0.400580333 0.156365767 0.001
## 11 water_mites 0.174129802 0.320100166 0.001
## 12 damselfly_nymphs 0.377049938 0.385621657 0.001
## 13 alderfly_larvae -0.310111451 0.280640675 0.001
## 14 crayfish -0.402506234 0.340251469 0.001
## 15 dragonfly_nymphs -0.165020260 0.534136734 0.001
## 16 riffle_beetles 0.262547260 0.578226863 0.001
## 17 midges 0.051990299 0.281117987 0.001
## 18 snails 0.519929061 0.296759927 0.001
## 19 fishfly_larvae -0.609185774 0.231001588 0.001
## 20 water_penny_beetles -0.206641534 0.574422704 0.001
## 21 cranefly_larvae -0.076679443 0.527004958 0.001
## 22 ameletidae 0.199386194 0.502826067 0.001
## 23 baetidae -0.040324032 0.557549578 0.001
## 24 heptageniidae -0.328412533 0.360655278 0.001
## 25 leptohyphidae -0.165700930 0.213683067 0.001
## 26 leptophlebiidae -0.713457664 0.141270040 0.001
## 27 chloroperlidae -0.224883231 0.363361837 0.001
## 28 leuctridae -0.555865682 0.446104508 0.001
## 29 nemouridae -0.702901939 0.202975744 0.001
## 30 perlidae -0.462233140 0.269901477 0.001
## 31 perlodidae -0.284367917 0.152668568 0.001
## 32 hydropsychidae -0.191548504 0.593862522 0.001
## 33 hydroptilidae 0.182670809 0.234543317 0.001
## 34 lepidostomatidae -0.762105710 -0.070256653 0.001
## 35 limnephilidae -0.607199064 0.015109537 0.001
## 36 molannidae -0.404309398 -0.043275970 0.001
## 37 odontoceridae -0.514622467 -0.053647895 0.001
## 38 philopotamidae -0.211335501 0.586719205 0.001
## 39 polycentropodidae 0.002732334 0.447883242 0.001
## 40 rhyacophilidae -0.620273067 0.188277541 0.001
## 41 uenoidae -0.496586095 0.312993942 0.001cooccurrence networks
summarizing all taxa
phwh_subset_biotic<-phwh %>%
dplyr::select(stream_name,date,mountain_dusky_larvae,mountain_dusky_juveniles,
mountain_dusky_adults,northern_dusky_larvae,northern_dusky_juveniles,northern_dusky_adults,two_lined_larvae,two_lined_juveniles,
two_lined_adults,long_tailed_larvae,long_tailed_juveniles,long_tailed_adults,northern_red_larvae,northern_red_juveniles,northern_red_adults,
mole_larvae,mole_juveniles,mole_adults,four_toed_larvae,four_toed_juveniles,four_toed_adults,red_backed_juveniles,red_backed_adults,
slimy_juveniles,slimy_adults,blacknose_dace,bluegill_sunfish,bluntnose_minnow,brindled_madtom,brook_stickleback,brook_trout,
central_mudminnow,central_stoneroller_minnow,common_shiner,creek_chub,eastern_sand_darter,fantail_darter,fathead_minnow,golden_shiner,
goldfish,grass_pickerel,greenside_darter,green_sunfish,johnny_darter,largemouth_bass,longear_sunfish,longnose_dace,mottled_sculpin,
northern_hog_sucker,pumpkinseed_sunfish,rainbow_darter,rainbow_trout,redbelly_dace,redside_dace,river_chub,silverjaw_minnow,smallmouth_bass,
spotfin_shiner,stonecat_madtom,striped_shiner,trout_perch,warmouth_sunfish,white_sucker,yellow_bullhead_catfish,unidentified_fish_fry,
sessile_animals,aquatic_worms,sow_bugs,scuds,water_mites,damselfly_nymphs,alderfly_larvae,other_beetles,crayfish,dragonfly_nymphs,
riffle_beetles,other_flies,midges,snails,clams,fishfly_larvae,water_penny_beetles,cranefly_larvae,ameletidae,arthropleidae,baetidae,
baetiscidae,caenidae,ephemerellidae,ephemeridae,heptageniidae,isonychiidae,leptohyphidae,leptophlebiidae,polymitarcyidae,potamanthidae,
pseudironidae,siphlonuridae,capniidae,chloroperlidae,leuctridae,nemouridae,peltoperlidae,perlidae,perlodidae,pteronarcyidae,taeniopterygidae,
brachycentridae,dipseuodopsidae,glossosomatidae,goeridae,helicopsychidae,hydropsychidae,hydroptilidae,lepidostomatidae,leptoceridae,
limnephilidae,molannidae,odontoceridae,philopotamidae,phryganeidae,polycentropodidae,psychomiidae,rhyacophilidae,uenoidae) %>%
distinct(stream_name, date, .keep_all = TRUE)
phwh_subset_biotic_2<-phwh_subset_biotic %>% replace(is.na(.),0) %>% replace(.=="abundant",30) %>% replace(.=="common",6) %>% replace(.=="rare", 1) %>% replace(.=="very abundant",50) %>%
distinct(stream_name, date, .keep_all = TRUE) %>% dplyr::select(-stream_name,-date)
phwh_subset_biotic_3<-data.frame(sapply(phwh_subset_biotic_2,as.numeric))
phwh_subset_biotic_3$stream_name<-phwh_subset_biotic$stream_name
phwh_subset_biotic_3$date<-phwh_subset_biotic$date
streambiotic<-phwh_subset_biotic_3
streambioticsum<-streambiotic %>%
group_by(stream_name) %>%
dplyr::select(!date) %>%
summarise_all(list(sum))
streambioticsum_suff <-streambioticsum[streambioticsum$stream_name %in% greenlightsites$stream_nam , ]
streambioticsum_suff_mtdusky<-streambioticsum_suff$mountain_dusky_larvae+
streambioticsum_suff$mountain_dusky_juveniles+
streambioticsum_suff$mountain_dusky_adults
streambioticsum_suff_nodusky<-streambioticsum_suff$northern_dusky_larvae+
streambioticsum_suff$northern_dusky_juveniles+
streambioticsum_suff$northern_dusky_adults
streambioticsum_suff_twolined<-streambioticsum_suff$two_lined_larvae+
streambioticsum_suff$two_lined_juveniles+
streambioticsum_suff$two_lined_adults
streambioticsum_suff_longtail<-streambioticsum_suff$long_tailed_larvae+
streambioticsum_suff$long_tailed_juveniles+
streambioticsum_suff$long_tailed_adults
streambioticsum_suff_red<-streambioticsum_suff$northern_red_larvae+
streambioticsum_suff$northern_red_juveniles+
streambioticsum_suff$northern_red_adults
streambioticsum_suff_mole<-streambioticsum_suff$mole_larvae+
streambioticsum_suff$mole_juveniles+
streambioticsum_suff$mole_adults
streambioticsum_suff_fourtoed<-streambioticsum_suff$four_toed_larvae+
streambioticsum_suff$four_toed_juveniles+
streambioticsum_suff$four_toed_adults
streambioticsum_suff_redback<-streambioticsum_suff$red_backed_juveniles+
streambioticsum_suff$red_backed_adults
streambioticsum_suff_slimy<-streambioticsum_suff$slimy_juveniles+
streambioticsum_suff$slimy_adults
streambioticsum_suff_sal<-data.frame(streambioticsum_suff_mtdusky,streambioticsum_suff_nodusky,streambioticsum_suff_twolined,
streambioticsum_suff_longtail,streambioticsum_suff_red,streambioticsum_suff_mole,
streambioticsum_suff_fourtoed,streambioticsum_suff_redback,streambioticsum_suff_slimy)
streambioticsum_suff_fish<-streambioticsum_suff[,27:65]
streambioticsum_suff_macro<-streambioticsum_suff[,67:126]
streambioticsum_suff_biotic<-data.frame(streambioticsum_suff_sal,streambioticsum_suff_fish,streambioticsum_suff_macro)
streambioticsum_suff_biotic_bin<- streambioticsum_suff_biotic %>% mutate_if(is.numeric, ~1 * (. >= 1))
streambioticsum_suff_biotic_bin_t<-t(streambioticsum_suff_biotic_bin)library(netassoc)
m_obs<-matrix(streambioticsum_suff_biotic_bin_t,ncol=137,nrow=108)
m_obs<-m_obs[rowSums(m_obs[])>0,]
m_obs<-m_obs[rowSums(m_obs[])<137,]
# Number of m species
nsp <- nrow(m_obs)
# Number of n sites
nsi <- ncol(m_obs)
m_nul <- floor(matrix(rbernoulli(nsp*nsi,p=0.5),ncol=nsi,nrow=nsp))
n <- make_netassoc_network(m_obs, m_nul,
method="partial_correlation",
args=list(method="shrinkage"), # for alternative estimators see ?partial_correlation
p.method='fdr',
numnulls=100,
plot=TRUE,
alpha=0.001)
## Calculating observed co-occurrence scores...
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## Calculating standardized effect sizes...
## Adjusting p-values for multiple comparisons...
## Building network...
n$network_all## IGRAPH 2c0e1a3 DNW- 61 2608 --
## + attr: name (v/c), weight (e/n)
## + edges from 2c0e1a3 (vertex names):
## [1] Species 1->Species 2 Species 1->Species 3 Species 1->Species 4 Species 1->Species 5 Species 1->Species 6 Species 1->Species 7 Species 1->Species 8 Species 1->Species 9 Species 1->Species 10 Species 1->Species 13 Species 1->Species 15 Species 1->Species 17 Species 1->Species 18 Species 1->Species 19 Species 1->Species 21 Species 1->Species 22 Species 1->Species 23 Species 1->Species 25 Species 1->Species 26 Species 1->Species 27 Species 1->Species 29 Species 1->Species 30 Species 1->Species 31 Species 1->Species 32 Species 1->Species 33 Species 1->Species 34 Species 1->Species 36
## [28] Species 1->Species 37 Species 1->Species 38 Species 1->Species 42 Species 1->Species 43 Species 1->Species 44 Species 1->Species 46 Species 1->Species 47 Species 1->Species 48 Species 1->Species 49 Species 1->Species 50 Species 1->Species 54 Species 1->Species 55 Species 1->Species 56 Species 1->Species 58 Species 2->Species 1 Species 2->Species 3 Species 2->Species 4 Species 2->Species 6 Species 2->Species 7 Species 2->Species 8 Species 2->Species 9 Species 2->Species 10 Species 2->Species 12 Species 2->Species 13 Species 2->Species 14 Species 2->Species 16 Species 2->Species 17
## [55] Species 2->Species 18 Species 2->Species 19 Species 2->Species 24 Species 2->Species 25 Species 2->Species 27 Species 2->Species 28 Species 2->Species 29 Species 2->Species 30 Species 2->Species 32 Species 2->Species 33 Species 2->Species 34 Species 2->Species 35 Species 2->Species 36 Species 2->Species 37 Species 2->Species 39 Species 2->Species 40 Species 2->Species 41 Species 2->Species 43 Species 2->Species 44 Species 2->Species 45 Species 2->Species 46 Species 2->Species 47 Species 2->Species 48 Species 2->Species 49 Species 2->Species 50 Species 2->Species 51 Species 2->Species 53
## [82] Species 2->Species 55 Species 2->Species 56 Species 2->Species 58 Species 2->Species 59 Species 2->Species 61 Species 3->Species 1 Species 3->Species 2 Species 3->Species 5 Species 3->Species 7 Species 3->Species 9 Species 3->Species 10 Species 3->Species 11 Species 3->Species 12 Species 3->Species 13 Species 3->Species 15 Species 3->Species 16 Species 3->Species 18 Species 3->Species 21 Species 3->Species 22 Species 3->Species 25 Species 3->Species 26 Species 3->Species 27 Species 3->Species 28 Species 3->Species 29 Species 3->Species 31 Species 3->Species 32 Species 3->Species 33
## [109] Species 3->Species 36 Species 3->Species 37 Species 3->Species 38 Species 3->Species 39 Species 3->Species 40 Species 3->Species 41 Species 3->Species 42 Species 3->Species 43 Species 3->Species 44 Species 3->Species 47 Species 3->Species 48 Species 3->Species 50 Species 3->Species 51 Species 3->Species 52 Species 3->Species 53 Species 3->Species 57 Species 3->Species 59 Species 3->Species 61 Species 4->Species 1 Species 4->Species 2 Species 4->Species 5 Species 4->Species 6 Species 4->Species 7 Species 4->Species 8 Species 4->Species 14 Species 4->Species 16 Species 4->Species 20
## [136] Species 4->Species 22 Species 4->Species 24 Species 4->Species 25 Species 4->Species 26 Species 4->Species 28 Species 4->Species 30 Species 4->Species 31 Species 4->Species 32 Species 4->Species 34 Species 4->Species 35 Species 4->Species 36 Species 4->Species 38 Species 4->Species 39 Species 4->Species 40 Species 4->Species 42 Species 4->Species 44 Species 4->Species 45 Species 4->Species 47 Species 4->Species 48 Species 4->Species 50 Species 4->Species 51 Species 4->Species 52 Species 4->Species 53 Species 4->Species 54 Species 4->Species 55 Species 4->Species 56 Species 4->Species 57
## [163] Species 4->Species 58 Species 4->Species 60 Species 5->Species 1 Species 5->Species 3 Species 5->Species 4 Species 5->Species 6 Species 5->Species 7 Species 5->Species 12 Species 5->Species 13 Species 5->Species 14 Species 5->Species 15 Species 5->Species 16 Species 5->Species 17 Species 5->Species 18 Species 5->Species 19 Species 5->Species 20 Species 5->Species 21 Species 5->Species 22 Species 5->Species 23 Species 5->Species 25 Species 5->Species 26 Species 5->Species 27 Species 5->Species 28 Species 5->Species 29 Species 5->Species 31 Species 5->Species 32 Species 5->Species 33
## [190] Species 5->Species 35 Species 5->Species 37 Species 5->Species 39 Species 5->Species 40 Species 5->Species 41 Species 5->Species 42 Species 5->Species 43 Species 5->Species 46 Species 5->Species 47 Species 5->Species 48 Species 5->Species 49 Species 5->Species 50 Species 5->Species 52 Species 5->Species 53 Species 5->Species 54 Species 5->Species 55 Species 5->Species 56 Species 5->Species 57 Species 5->Species 58 Species 5->Species 60 Species 6->Species 1 Species 6->Species 2 Species 6->Species 4 Species 6->Species 5 Species 6->Species 7 Species 6->Species 8 Species 6->Species 9
## + ... omitted several edgesplot(n$network_all)
# presence/absence across plots
streambioticsum_suff_biotic_bin_t_2<-streambioticsum_suff_biotic_bin_t[rowSums(streambioticsum_suff_biotic_bin_t[])>0,]
streambioticsum_suff_biotic_bin_t_3<-streambioticsum_suff_biotic_bin_t_2[rowSums(streambioticsum_suff_biotic_bin_t_2[])<137,]
library(cooccur)
library(igraph)
co <- print(cooccur(streambioticsum_suff_biotic_bin_t_3, spp_names = TRUE))##
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## Call:
## cooccur(mat = streambioticsum_suff_biotic_bin_t_3, spp_names = TRUE)
##
## Of 1653 species pair combinations, 721 pairs (43.62 %) were removed from the analysis because expected co-occurrence was < 1 and 932 pairs were analyzed
##
## Cooccurrence Table:
## sp1 sp2 sp1_inc sp2_inc obs_cooccur prob_cooccur exp_cooccur p_lt p_gt sp1_name sp2_name
## 1 1 2 40 74 34 0.080 15.4 1.00000 0.00000 streambioticsum_suff_mtdusky streambioticsum_suff_nodusky
## 2 1 3 40 133 36 0.144 27.7 0.99988 0.00069 streambioticsum_suff_mtdusky streambioticsum_suff_twolined
## 5 1 7 40 70 9 0.076 14.6 0.02800 0.98941 streambioticsum_suff_mtdusky streambioticsum_suff_redback
## 8 1 13 40 43 3 0.047 9.0 0.00650 0.99871 streambioticsum_suff_mtdusky creek_chub
## 16 1 27 40 79 24 0.086 16.5 0.99806 0.00574 streambioticsum_suff_mtdusky alderfly_larvae
## 18 1 29 40 101 15 0.110 21.0 0.02416 0.99020 streambioticsum_suff_mtdusky riffle_beetles
## 21 1 32 40 68 20 0.074 14.2 0.98988 0.02500 streambioticsum_suff_mtdusky fishfly_larvae
## 26 1 38 40 50 4 0.054 10.4 0.00566 0.99872 streambioticsum_suff_mtdusky heptageniidae
## 30 1 43 40 93 29 0.101 19.4 0.99987 0.00052 streambioticsum_suff_mtdusky leuctridae
## 32 1 45 40 51 5 0.055 10.6 0.01590 0.99545 streambioticsum_suff_mtdusky perlidae
## 33 1 46 40 22 1 0.024 4.6 0.03199 0.99585 streambioticsum_suff_mtdusky perlodidae
## 36 1 51 40 52 1 0.056 10.8 0.00001 1.00000 streambioticsum_suff_mtdusky limnephilidae
## 40 1 56 40 81 26 0.088 16.9 0.99972 0.00099 streambioticsum_suff_mtdusky polycentropodidae
## 43 2 3 74 133 64 0.267 51.3 1.00000 0.00002 streambioticsum_suff_nodusky streambioticsum_suff_twolined
## 45 2 5 74 18 12 0.036 6.9 0.99741 0.01097 streambioticsum_suff_nodusky streambioticsum_suff_red
## 47 2 8 74 8 7 0.016 3.1 0.99962 0.00576 streambioticsum_suff_nodusky streambioticsum_suff_slimy
## 50 2 13 74 43 11 0.086 16.6 0.03378 0.98613 streambioticsum_suff_nodusky creek_chub
## 59 2 27 74 79 42 0.159 30.4 0.99986 0.00044 streambioticsum_suff_nodusky alderfly_larvae
## 64 2 32 74 68 39 0.137 26.2 0.99998 0.00007 streambioticsum_suff_nodusky fishfly_larvae
## 65 2 33 74 47 26 0.094 18.1 0.99792 0.00577 streambioticsum_suff_nodusky water_penny_beetles
## 66 2 34 74 70 34 0.141 27.0 0.98955 0.02257 streambioticsum_suff_nodusky ameletidae
## 72 2 40 74 73 39 0.147 28.1 0.99973 0.00079 streambioticsum_suff_nodusky leptophlebiidae
## 74 2 43 74 93 48 0.187 35.8 0.99992 0.00026 streambioticsum_suff_nodusky leuctridae
## 75 2 44 74 61 32 0.122 23.5 0.99779 0.00568 streambioticsum_suff_nodusky nemouridae
## 83 2 53 74 6 5 0.012 2.3 0.99712 0.03244 streambioticsum_suff_nodusky odontoceridae
## 84 2 54 74 74 37 0.149 28.5 0.99683 0.00765 streambioticsum_suff_nodusky philopotamidae
## 86 2 56 74 81 42 0.163 31.2 0.99965 0.00101 streambioticsum_suff_nodusky polycentropodidae
## 87 2 57 74 27 19 0.054 10.4 0.99994 0.00032 streambioticsum_suff_nodusky rhyacophilidae
## 88 2 58 74 54 33 0.108 20.8 0.99998 0.00007 streambioticsum_suff_nodusky uenoidae
## 90 3 5 133 18 17 0.065 12.5 0.99908 0.00935 streambioticsum_suff_twolined streambioticsum_suff_red
## 92 3 7 133 70 57 0.253 48.5 0.99864 0.00399 streambioticsum_suff_twolined streambioticsum_suff_redback
## 93 3 8 133 8 8 0.029 5.5 1.00000 0.04959 streambioticsum_suff_twolined streambioticsum_suff_slimy
## 94 3 9 133 25 22 0.090 17.3 0.99524 0.02079 streambioticsum_suff_twolined blacknose_dace
## 97 3 13 133 43 38 0.155 29.8 0.99976 0.00113 streambioticsum_suff_twolined creek_chub
## 108 3 27 133 79 68 0.285 54.7 1.00000 0.00001 streambioticsum_suff_twolined alderfly_larvae
## 109 3 28 133 125 98 0.451 86.6 0.99994 0.00020 streambioticsum_suff_twolined dragonfly_nymphs
## 110 3 29 133 101 82 0.364 70.0 0.99996 0.00014 streambioticsum_suff_twolined riffle_beetles
## 113 3 32 133 68 60 0.245 47.1 1.00000 0.00001 streambioticsum_suff_twolined fishfly_larvae
## 114 3 33 133 47 45 0.170 32.6 1.00000 0.00000 streambioticsum_suff_twolined water_penny_beetles
## 115 3 34 133 70 61 0.253 48.5 1.00000 0.00002 streambioticsum_suff_twolined ameletidae
## 119 3 38 133 50 42 0.180 34.6 0.99825 0.00576 streambioticsum_suff_twolined heptageniidae
## 121 3 40 133 73 68 0.263 50.6 1.00000 0.00000 streambioticsum_suff_twolined leptophlebiidae
## 122 3 42 133 22 20 0.079 15.2 0.99783 0.01294 streambioticsum_suff_twolined chloroperlidae
## 123 3 43 133 93 82 0.336 64.4 1.00000 0.00000 streambioticsum_suff_twolined leuctridae
## 124 3 44 133 61 53 0.220 42.3 0.99996 0.00017 streambioticsum_suff_twolined nemouridae
## 125 3 45 133 51 47 0.184 35.3 1.00000 0.00001 streambioticsum_suff_twolined perlidae
## 129 3 50 133 35 32 0.126 24.2 0.99988 0.00078 streambioticsum_suff_twolined lepidostomatidae
## 130 3 51 133 52 43 0.188 36.0 0.99678 0.00959 streambioticsum_suff_twolined limnephilidae
## 133 3 54 133 74 68 0.267 51.3 1.00000 0.00000 streambioticsum_suff_twolined philopotamidae
## 135 3 56 133 81 70 0.292 56.1 1.00000 0.00001 streambioticsum_suff_twolined polycentropodidae
## 136 3 57 133 27 25 0.097 18.7 0.99969 0.00238 streambioticsum_suff_twolined rhyacophilidae
## 137 3 58 133 54 48 0.195 37.4 0.99998 0.00011 streambioticsum_suff_twolined uenoidae
## 138 4 7 6 70 6 0.011 2.2 1.00000 0.00204 streambioticsum_suff_longtail streambioticsum_suff_redback
## 144 4 27 6 79 5 0.013 2.5 0.99568 0.04393 streambioticsum_suff_longtail alderfly_larvae
## 147 4 30 6 131 1 0.021 4.1 0.01298 0.99914 streambioticsum_suff_longtail snails
## 149 4 32 6 68 5 0.011 2.1 0.99830 0.02180 streambioticsum_suff_longtail fishfly_larvae
## 150 4 33 6 47 4 0.008 1.5 0.99637 0.03259 streambioticsum_suff_longtail water_penny_beetles
## 153 4 40 6 73 5 0.012 2.3 0.99735 0.03045 streambioticsum_suff_longtail leptophlebiidae
## 155 4 44 6 61 5 0.010 1.9 0.99914 0.01298 streambioticsum_suff_longtail nemouridae
## 157 4 50 6 35 4 0.006 1.1 0.99918 0.01079 streambioticsum_suff_longtail lepidostomatidae
## 158 4 51 6 52 5 0.008 1.6 0.99968 0.00598 streambioticsum_suff_longtail limnephilidae
## 162 5 7 18 70 17 0.034 6.6 1.00000 0.00000 streambioticsum_suff_red streambioticsum_suff_redback
## 164 5 13 18 43 0 0.021 4.0 0.00814 1.00000 streambioticsum_suff_red creek_chub
## 165 5 23 18 53 1 0.026 5.0 0.01896 0.99785 streambioticsum_suff_red sow_bugs
## 168 5 26 18 97 4 0.047 9.1 0.01042 0.99779 streambioticsum_suff_red damselfly_nymphs
## 170 5 28 18 125 17 0.061 11.7 0.99972 0.00338 streambioticsum_suff_red dragonfly_nymphs
## 172 5 30 18 131 4 0.064 12.3 0.00003 1.00000 streambioticsum_suff_red snails
## 174 5 32 18 68 14 0.033 6.4 0.99998 0.00015 streambioticsum_suff_red fishfly_larvae
## 176 5 34 18 70 1 0.034 6.6 0.00226 0.99983 streambioticsum_suff_red ameletidae
## 180 5 40 18 73 17 0.036 6.8 1.00000 0.00000 streambioticsum_suff_red leptophlebiidae
## 182 5 43 18 93 17 0.045 8.7 1.00000 0.00002 streambioticsum_suff_red leuctridae
## 183 5 44 18 61 15 0.030 5.7 1.00000 0.00000 streambioticsum_suff_red nemouridae
## 187 5 50 18 35 17 0.017 3.3 1.00000 0.00000 streambioticsum_suff_red lepidostomatidae
## 188 5 51 18 52 16 0.025 4.9 1.00000 0.00000 streambioticsum_suff_red limnephilidae
## 189 5 54 18 74 14 0.036 6.9 0.99994 0.00047 streambioticsum_suff_red philopotamidae
## 191 5 57 18 27 12 0.013 2.5 1.00000 0.00000 streambioticsum_suff_red rhyacophilidae
## 192 5 58 18 54 11 0.026 5.1 0.99960 0.00219 streambioticsum_suff_red uenoidae
## 197 7 8 70 8 7 0.015 2.9 0.99976 0.00394 streambioticsum_suff_redback streambioticsum_suff_slimy
## 198 7 9 70 25 14 0.047 9.1 0.99086 0.02696 streambioticsum_suff_redback blacknose_dace
## 209 7 27 70 79 39 0.150 28.8 0.99943 0.00159 streambioticsum_suff_redback alderfly_larvae
## 212 7 30 70 131 36 0.249 47.8 0.00016 0.99996 streambioticsum_suff_redback snails
## 214 7 32 70 68 38 0.129 24.8 0.99999 0.00004 streambioticsum_suff_redback fishfly_larvae
## 215 7 33 70 47 26 0.089 17.1 0.99937 0.00197 streambioticsum_suff_redback water_penny_beetles
## 219 7 37 70 3 3 0.006 1.1 1.00000 0.04714 streambioticsum_suff_redback ephemeridae
## 220 7 38 70 50 28 0.095 18.2 0.99974 0.00087 streambioticsum_suff_redback heptageniidae
## 222 7 40 70 73 42 0.139 26.6 1.00000 0.00000 streambioticsum_suff_redback leptophlebiidae
## 223 7 42 70 22 14 0.042 8.0 0.99862 0.00566 streambioticsum_suff_redback chloroperlidae
## 224 7 43 70 93 45 0.177 33.9 0.99976 0.00071 streambioticsum_suff_redback leuctridae
## 225 7 44 70 61 35 0.116 22.2 0.99999 0.00004 streambioticsum_suff_redback nemouridae
## 226 7 45 70 51 32 0.097 18.6 1.00000 0.00001 streambioticsum_suff_redback perlidae
## 230 7 50 70 35 25 0.066 12.8 1.00000 0.00000 streambioticsum_suff_redback lepidostomatidae
## 231 7 51 70 52 33 0.099 19.0 1.00000 0.00000 streambioticsum_suff_redback limnephilidae
## 232 7 52 70 4 4 0.008 1.5 1.00000 0.01671 streambioticsum_suff_redback molannidae
## 233 7 53 70 6 6 0.011 2.2 1.00000 0.00204 streambioticsum_suff_redback odontoceridae
## 234 7 54 70 74 34 0.141 27.0 0.98955 0.02257 streambioticsum_suff_redback philopotamidae
## 237 7 57 70 27 17 0.051 9.8 0.99941 0.00240 streambioticsum_suff_redback rhyacophilidae
## 238 7 58 70 54 35 0.103 19.7 1.00000 0.00000 streambioticsum_suff_redback uenoidae
## 239 8 9 8 25 4 0.005 1.0 0.99891 0.01110 streambioticsum_suff_slimy blacknose_dace
## 243 8 25 8 65 6 0.014 2.7 0.99763 0.01909 streambioticsum_suff_slimy water_mites
## 247 8 29 8 101 7 0.022 4.2 0.99489 0.04473 streambioticsum_suff_slimy riffle_beetles
## 250 8 32 8 68 6 0.015 2.8 0.99677 0.02436 streambioticsum_suff_slimy fishfly_larvae
## 251 8 33 8 47 5 0.010 2.0 0.99692 0.02240 streambioticsum_suff_slimy water_penny_beetles
## 254 8 40 8 73 7 0.016 3.0 0.99966 0.00525 streambioticsum_suff_slimy leptophlebiidae
## 255 8 43 8 93 7 0.020 3.9 0.99742 0.02632 streambioticsum_suff_slimy leuctridae
## 256 8 44 8 61 6 0.013 2.5 0.99848 0.01349 streambioticsum_suff_slimy nemouridae
## 260 8 54 8 74 7 0.016 3.1 0.99962 0.00576 streambioticsum_suff_slimy philopotamidae
## 261 8 56 8 81 7 0.018 3.4 0.99919 0.01058 streambioticsum_suff_slimy polycentropodidae
## 262 8 57 8 27 4 0.006 1.1 0.99839 0.01483 streambioticsum_suff_slimy rhyacophilidae
## 263 8 58 8 54 6 0.012 2.2 0.99936 0.00682 streambioticsum_suff_slimy uenoidae
## 264 9 13 25 43 15 0.029 5.6 1.00000 0.00001 blacknose_dace creek_chub
## 268 9 26 25 97 19 0.066 12.6 0.99872 0.00527 blacknose_dace damselfly_nymphs
## 271 9 29 25 101 23 0.068 13.2 1.00000 0.00001 blacknose_dace riffle_beetles
## 275 9 33 25 47 11 0.032 6.1 0.99471 0.01776 blacknose_dace water_penny_beetles
## 276 9 34 25 70 16 0.047 9.1 0.99938 0.00261 blacknose_dace ameletidae
## 278 9 38 25 50 14 0.034 6.5 0.99988 0.00061 blacknose_dace heptageniidae
## 279 9 39 25 12 5 0.008 1.6 0.99854 0.01058 blacknose_dace leptohyphidae
## 280 9 40 25 73 14 0.050 9.5 0.98533 0.04028 blacknose_dace leptophlebiidae
## 281 9 42 25 22 9 0.015 2.9 0.99995 0.00042 blacknose_dace chloroperlidae
## 282 9 43 25 93 19 0.063 12.1 0.99940 0.00272 blacknose_dace leuctridae
## 283 9 44 25 61 15 0.041 7.9 0.99961 0.00172 blacknose_dace nemouridae
## 284 9 45 25 51 15 0.035 6.6 0.99997 0.00015 blacknose_dace perlidae
## 285 9 46 25 22 8 0.015 2.9 0.99958 0.00256 blacknose_dace perlodidae
## 287 9 50 25 35 0 0.024 4.6 0.00444 1.00000 blacknose_dace lepidostomatidae
## 289 9 54 25 74 14 0.050 9.6 0.98296 0.04570 blacknose_dace philopotamidae
## 318 13 15 43 5 4 0.006 1.1 0.99953 0.00938 creek_chub johnny_darter
## 320 13 22 43 5 4 0.006 1.1 0.99953 0.00938 creek_chub sessile_animals
## 324 13 26 43 97 32 0.113 21.7 0.99993 0.00030 creek_chub damselfly_nymphs
## 325 13 27 43 79 24 0.092 17.7 0.99138 0.02104 creek_chub alderfly_larvae
## 326 13 28 43 125 38 0.146 28.0 0.99998 0.00013 creek_chub dragonfly_nymphs
## 327 13 29 43 101 41 0.118 22.6 1.00000 0.00000 creek_chub riffle_beetles
## 328 13 30 43 131 39 0.153 29.3 0.99998 0.00013 creek_chub snails
## 329 13 31 43 55 18 0.064 12.3 0.98976 0.02552 creek_chub clams
## 332 13 34 43 70 26 0.082 15.7 0.99994 0.00025 creek_chub ameletidae
## 333 13 35 43 13 6 0.015 2.9 0.98948 0.04399 creek_chub caenidae
## 335 13 38 43 50 19 0.058 11.2 0.99923 0.00260 creek_chub heptageniidae
## 339 13 43 43 93 27 0.108 20.8 0.98980 0.02450 creek_chub leuctridae
## 341 13 45 43 51 22 0.059 11.4 0.99998 0.00007 creek_chub perlidae
## 344 13 50 43 35 3 0.041 7.8 0.02005 0.99532 creek_chub lepidostomatidae
## 345 13 51 43 52 18 0.061 11.6 0.99534 0.01287 creek_chub limnephilidae
## 347 13 54 43 74 23 0.086 16.6 0.99269 0.01823 creek_chub philopotamidae
## 348 13 55 43 9 5 0.010 2.0 0.99546 0.02804 creek_chub phryganeidae
## 362 15 29 5 101 5 0.014 2.6 1.00000 0.03839 johnny_darter riffle_beetles
## 370 15 43 5 93 5 0.013 2.4 1.00000 0.02519 johnny_darter leuctridae
## 383 16 29 5 101 5 0.014 2.6 1.00000 0.03839 rainbow_darter riffle_beetles
## 412 20 34 4 70 4 0.008 1.5 1.00000 0.01671 white_sucker ameletidae
## 443 23 24 53 60 33 0.086 16.6 1.00000 0.00000 sow_bugs scuds
## 444 23 25 53 65 30 0.093 17.9 0.99999 0.00005 sow_bugs water_mites
## 445 23 26 53 97 33 0.139 26.8 0.98534 0.03196 sow_bugs damselfly_nymphs
## 449 23 30 53 131 43 0.188 36.2 0.99561 0.01228 sow_bugs snails
## 450 23 31 53 55 22 0.079 15.2 0.99487 0.01314 sow_bugs clams
## 451 23 32 53 68 8 0.098 18.8 0.00015 0.99997 sow_bugs fishfly_larvae
## 458 23 40 53 73 9 0.105 20.2 0.00012 0.99997 sow_bugs leptophlebiidae
## 460 23 43 53 93 20 0.134 25.7 0.04700 0.97732 sow_bugs leuctridae
## 461 23 44 53 61 6 0.088 16.8 0.00008 0.99999 sow_bugs nemouridae
## 462 23 45 53 51 7 0.073 14.1 0.00633 0.99815 sow_bugs perlidae
## 463 23 46 53 22 1 0.032 6.1 0.00539 0.99950 sow_bugs perlodidae
## 464 23 49 53 22 11 0.032 6.1 0.99576 0.01518 sow_bugs hydroptilidae
## 465 23 50 53 35 3 0.050 9.7 0.00290 0.99949 sow_bugs lepidostomatidae
## 466 23 51 53 52 7 0.075 14.4 0.00487 0.99862 sow_bugs limnephilidae
## 472 23 57 53 27 2 0.039 7.5 0.00660 0.99898 sow_bugs rhyacophilidae
## 473 23 58 53 54 9 0.078 14.9 0.02359 0.99115 sow_bugs uenoidae
## 474 24 25 60 65 31 0.106 20.3 0.99986 0.00046 scuds water_mites
## 476 24 27 60 79 15 0.129 24.7 0.00157 0.99949 scuds alderfly_larvae
## 480 24 31 60 55 25 0.090 17.2 0.99760 0.00649 scuds clams
## 481 24 32 60 68 13 0.111 21.2 0.00508 0.99822 scuds fishfly_larvae
## 486 24 38 60 50 7 0.081 15.6 0.00133 0.99967 scuds heptageniidae
## 487 24 39 60 12 0 0.020 3.8 0.00946 1.00000 scuds leptohyphidae
## 488 24 40 60 73 11 0.119 22.8 0.00009 0.99998 scuds leptophlebiidae
## 490 24 43 60 93 22 0.151 29.1 0.02012 0.99103 scuds leuctridae
## 491 24 44 60 61 12 0.099 19.1 0.01271 0.99519 scuds nemouridae
## 492 24 45 60 51 5 0.083 15.9 0.00005 0.99999 scuds perlidae
## 506 25 28 65 125 48 0.220 42.3 0.97719 0.04746 water_mites dragonfly_nymphs
## 507 25 29 65 101 47 0.178 34.2 0.99998 0.00007 water_mites riffle_beetles
## 508 25 30 65 131 50 0.231 44.3 0.97941 0.04431 water_mites snails
## 525 25 49 65 22 12 0.039 7.4 0.99092 0.02844 water_mites hydroptilidae
## 530 25 54 65 74 33 0.130 25.1 0.99580 0.01006 water_mites philopotamidae
## 536 26 28 97 125 78 0.329 63.2 1.00000 0.00001 damselfly_nymphs dragonfly_nymphs
## 537 26 29 97 101 69 0.266 51.0 1.00000 0.00000 damselfly_nymphs riffle_beetles
## 538 26 30 97 131 77 0.345 66.2 0.99980 0.00064 damselfly_nymphs snails
## 539 26 31 97 55 37 0.145 27.8 0.99912 0.00256 damselfly_nymphs clams
## 542 26 34 97 70 44 0.184 35.4 0.99703 0.00719 damselfly_nymphs ameletidae
## 543 26 35 97 13 12 0.034 6.6 0.99991 0.00142 damselfly_nymphs caenidae
## 548 26 40 97 73 30 0.192 36.9 0.02875 0.98605 damselfly_nymphs leptophlebiidae
## 556 26 50 97 35 11 0.092 17.7 0.01000 0.99664 damselfly_nymphs lepidostomatidae
## 565 27 28 79 125 64 0.268 51.4 0.99998 0.00008 alderfly_larvae dragonfly_nymphs
## 566 27 29 79 101 50 0.216 41.6 0.99582 0.00964 alderfly_larvae riffle_beetles
## 569 27 32 79 68 49 0.146 28.0 1.00000 0.00000 alderfly_larvae fishfly_larvae
## 570 27 33 79 47 33 0.101 19.3 1.00000 0.00000 alderfly_larvae water_penny_beetles
## 571 27 34 79 70 37 0.150 28.8 0.99592 0.00962 alderfly_larvae ameletidae
## 575 27 38 79 50 31 0.107 20.6 0.99986 0.00048 alderfly_larvae heptageniidae
## 576 27 39 79 12 11 0.026 4.9 0.99999 0.00029 alderfly_larvae leptohyphidae
## 577 27 40 79 73 47 0.156 30.0 1.00000 0.00000 alderfly_larvae leptophlebiidae
## 578 27 42 79 22 15 0.047 9.1 0.99844 0.00634 alderfly_larvae chloroperlidae
## 579 27 43 79 93 59 0.199 38.3 1.00000 0.00000 alderfly_larvae leuctridae
## 580 27 44 79 61 38 0.131 25.1 0.99999 0.00005 alderfly_larvae nemouridae
## 581 27 45 79 51 42 0.109 21.0 1.00000 0.00000 alderfly_larvae perlidae
## 586 27 51 79 52 30 0.111 21.4 0.99861 0.00388 alderfly_larvae limnephilidae
## 589 27 54 79 74 43 0.159 30.4 0.99996 0.00014 alderfly_larvae philopotamidae
## 590 27 55 79 9 7 0.019 3.7 0.99610 0.02661 alderfly_larvae phryganeidae
## 591 27 56 79 81 41 0.174 33.3 0.99237 0.01663 alderfly_larvae polycentropodidae
## 592 27 57 79 27 17 0.058 11.1 0.99631 0.01195 alderfly_larvae rhyacophilidae
## 593 27 58 79 54 39 0.116 22.2 1.00000 0.00000 alderfly_larvae uenoidae
## 594 28 29 125 101 80 0.342 65.8 1.00000 0.00001 dragonfly_nymphs riffle_beetles
## 596 28 31 125 55 43 0.186 35.8 0.99582 0.01123 dragonfly_nymphs clams
## 597 28 32 125 68 52 0.231 44.3 0.99594 0.01024 dragonfly_nymphs fishfly_larvae
## 598 28 33 125 47 43 0.159 30.6 1.00000 0.00000 dragonfly_nymphs water_penny_beetles
## 599 28 34 125 70 56 0.237 45.6 0.99978 0.00072 dragonfly_nymphs ameletidae
## 600 28 35 125 13 12 0.044 8.5 0.99700 0.02610 dragonfly_nymphs caenidae
## 603 28 38 125 50 44 0.170 32.6 0.99999 0.00003 dragonfly_nymphs heptageniidae
## 605 28 40 125 73 58 0.248 47.5 0.99976 0.00077 dragonfly_nymphs leptophlebiidae
## 606 28 42 125 22 19 0.075 14.3 0.99577 0.01909 dragonfly_nymphs chloroperlidae
## 607 28 43 125 93 76 0.315 60.5 1.00000 0.00000 dragonfly_nymphs leuctridae
## 608 28 44 125 61 51 0.207 39.7 0.99997 0.00014 dragonfly_nymphs nemouridae
## 609 28 45 125 51 46 0.173 33.2 1.00000 0.00000 dragonfly_nymphs perlidae
## 610 28 46 125 22 19 0.075 14.3 0.99577 0.01909 dragonfly_nymphs perlodidae
## 613 28 50 125 35 32 0.119 22.8 0.99999 0.00012 dragonfly_nymphs lepidostomatidae
## 614 28 51 125 52 44 0.176 33.9 0.99993 0.00032 dragonfly_nymphs limnephilidae
## 617 28 54 125 74 62 0.251 48.2 1.00000 0.00001 dragonfly_nymphs philopotamidae
## 620 28 57 125 27 25 0.092 17.6 0.99994 0.00054 dragonfly_nymphs rhyacophilidae
## 621 28 58 125 54 49 0.183 35.2 1.00000 0.00000 dragonfly_nymphs uenoidae
## 622 29 30 101 131 83 0.359 68.9 1.00000 0.00001 riffle_beetles snails
## 623 29 31 101 55 35 0.151 28.9 0.98256 0.03707 riffle_beetles clams
## 625 29 33 101 47 34 0.129 24.7 0.99958 0.00140 riffle_beetles water_penny_beetles
## 626 29 34 101 70 54 0.192 36.8 1.00000 0.00000 riffle_beetles ameletidae
## 630 29 38 101 50 37 0.137 26.3 0.99991 0.00033 riffle_beetles heptageniidae
## 631 29 39 101 12 10 0.033 6.3 0.99576 0.02557 riffle_beetles leptohyphidae
## 633 29 42 101 22 16 0.060 11.6 0.98864 0.03585 riffle_beetles chloroperlidae
## 634 29 43 101 93 59 0.255 48.9 0.99893 0.00273 riffle_beetles leuctridae
## 636 29 45 101 51 37 0.140 26.8 0.99981 0.00067 riffle_beetles perlidae
## 640 29 50 101 35 13 0.096 18.4 0.03283 0.98675 riffle_beetles lepidostomatidae
## 641 29 51 101 52 33 0.142 27.4 0.97770 0.04660 riffle_beetles limnephilidae
## 644 29 54 101 74 53 0.203 38.9 0.99999 0.00002 riffle_beetles philopotamidae
## 646 29 56 101 81 51 0.222 42.6 0.99549 0.01028 riffle_beetles polycentropodidae
## 648 29 58 101 54 35 0.148 28.4 0.98911 0.02458 riffle_beetles uenoidae
## 649 30 31 131 55 45 0.195 37.5 0.99761 0.00712 snails clams
## 650 30 32 131 68 40 0.242 46.4 0.02871 0.98678 snails fishfly_larvae
## 652 30 34 131 70 54 0.249 47.8 0.98604 0.03104 snails ameletidae
## 658 30 40 131 73 35 0.259 49.8 0.00000 1.00000 snails leptophlebiidae
## 660 30 43 131 93 57 0.330 63.5 0.03229 0.98461 snails leuctridae
## 661 30 44 131 61 30 0.217 41.6 0.00013 0.99997 snails nemouridae
## 665 30 49 131 22 19 0.078 15.0 0.99011 0.03906 snails hydroptilidae
## 666 30 50 131 35 12 0.124 23.9 0.00000 1.00000 snails lepidostomatidae
## 669 30 53 131 6 0 0.021 4.1 0.00086 1.00000 snails odontoceridae
## 697 31 58 55 54 21 0.081 15.5 0.98266 0.03857 clams uenoidae
## 698 32 33 68 47 33 0.087 16.6 1.00000 0.00000 fishfly_larvae water_penny_beetles
## 699 32 34 68 70 31 0.129 24.8 0.98188 0.03726 fishfly_larvae ameletidae
## 702 32 37 68 3 3 0.006 1.1 1.00000 0.04316 fishfly_larvae ephemeridae
## 703 32 38 68 50 25 0.092 17.7 0.99595 0.01041 fishfly_larvae heptageniidae
## 704 32 39 68 12 8 0.022 4.2 0.99520 0.02360 fishfly_larvae leptohyphidae
## 705 32 40 68 73 46 0.135 25.9 1.00000 0.00000 fishfly_larvae leptophlebiidae
## 706 32 42 68 22 13 0.041 7.8 0.99594 0.01429 fishfly_larvae chloroperlidae
## 707 32 43 68 93 59 0.172 32.9 1.00000 0.00000 fishfly_larvae leuctridae
## 708 32 44 68 61 44 0.113 21.6 1.00000 0.00000 fishfly_larvae nemouridae
## 709 32 45 68 51 34 0.094 18.1 1.00000 0.00000 fishfly_larvae perlidae
## 710 32 46 68 22 13 0.041 7.8 0.99594 0.01429 fishfly_larvae perlodidae
## 711 32 47 68 3 3 0.006 1.1 1.00000 0.04316 fishfly_larvae glossosomatidae
## 713 32 50 68 35 25 0.065 12.4 1.00000 0.00000 fishfly_larvae lepidostomatidae
## 714 32 51 68 52 32 0.096 18.4 1.00000 0.00001 fishfly_larvae limnephilidae
## 716 32 53 68 6 6 0.011 2.1 1.00000 0.00170 fishfly_larvae odontoceridae
## 717 32 54 68 74 44 0.137 26.2 1.00000 0.00000 fishfly_larvae philopotamidae
## 719 32 56 68 81 39 0.149 28.7 0.99952 0.00137 fishfly_larvae polycentropodidae
## 720 32 57 68 27 21 0.050 9.6 1.00000 0.00000 fishfly_larvae rhyacophilidae
## 721 32 58 68 54 35 0.100 19.1 1.00000 0.00000 fishfly_larvae uenoidae
## 722 33 34 47 70 28 0.089 17.1 0.99995 0.00018 water_penny_beetles ameletidae
## 725 33 38 47 50 22 0.064 12.2 0.99992 0.00030 water_penny_beetles heptageniidae
## 726 33 39 47 12 8 0.015 2.9 0.99980 0.00171 water_penny_beetles leptohyphidae
## 727 33 40 47 73 28 0.093 17.9 0.99986 0.00049 water_penny_beetles leptophlebiidae
## 728 33 42 47 22 12 0.028 5.4 0.99977 0.00122 water_penny_beetles chloroperlidae
## 729 33 43 47 93 43 0.119 22.8 1.00000 0.00000 water_penny_beetles leuctridae
## 730 33 44 47 61 27 0.078 14.9 1.00000 0.00002 water_penny_beetles nemouridae
## 731 33 45 47 51 24 0.065 12.5 0.99999 0.00003 water_penny_beetles perlidae
## 732 33 46 47 22 10 0.028 5.4 0.99473 0.01871 water_penny_beetles perlodidae
## 733 33 49 47 22 10 0.028 5.4 0.99473 0.01871 water_penny_beetles hydroptilidae
## 734 33 50 47 35 14 0.045 8.6 0.99356 0.01864 water_penny_beetles lepidostomatidae
## 735 33 51 47 52 18 0.066 12.7 0.98377 0.03783 water_penny_beetles limnephilidae
## 737 33 54 47 74 35 0.094 18.1 1.00000 0.00000 water_penny_beetles philopotamidae
## 739 33 56 47 81 31 0.103 19.8 0.99996 0.00015 water_penny_beetles polycentropodidae
## 740 33 57 47 27 16 0.034 6.6 1.00000 0.00003 water_penny_beetles rhyacophilidae
## 741 33 58 47 54 29 0.069 13.2 1.00000 0.00000 water_penny_beetles uenoidae
## 743 34 36 70 5 5 0.009 1.8 1.00000 0.00587 ameletidae ephemerellidae
## 745 34 38 70 50 32 0.095 18.2 1.00000 0.00000 ameletidae heptageniidae
## 746 34 39 70 12 9 0.023 4.4 0.99913 0.00607 ameletidae leptohyphidae
## 747 34 40 70 73 38 0.139 26.6 0.99987 0.00040 ameletidae leptophlebiidae
## 748 34 42 70 22 13 0.042 8.0 0.99434 0.01896 ameletidae chloroperlidae
## 749 34 43 70 93 44 0.177 33.9 0.99929 0.00194 ameletidae leuctridae
## 750 34 44 70 61 29 0.116 22.2 0.98992 0.02248 ameletidae nemouridae
## 751 34 45 70 51 26 0.097 18.6 0.99606 0.01007 ameletidae perlidae
## 752 34 46 70 22 15 0.042 8.0 0.99973 0.00138 ameletidae perlodidae
## 759 34 54 70 74 33 0.141 27.0 0.97743 0.04485 ameletidae philopotamidae
## 761 34 56 70 81 40 0.154 29.5 0.99956 0.00125 ameletidae polycentropodidae
## 763 34 58 70 54 31 0.103 19.7 0.99995 0.00018 ameletidae uenoidae
## 769 35 45 13 51 7 0.018 3.5 0.99355 0.02866 caenidae perlidae
## 770 35 46 13 22 4 0.008 1.5 0.99129 0.04644 caenidae perlodidae
## 777 35 58 13 54 7 0.019 3.7 0.99038 0.03941 caenidae uenoidae
## 791 38 39 50 12 8 0.016 3.1 0.99965 0.00271 heptageniidae leptohyphidae
## 792 38 40 50 73 39 0.099 19.0 1.00000 0.00000 heptageniidae leptophlebiidae
## 793 38 42 50 22 13 0.030 5.7 0.99992 0.00049 heptageniidae chloroperlidae
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## 795 38 44 50 61 32 0.083 15.9 1.00000 0.00000 heptageniidae nemouridae
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## 797 38 46 50 22 15 0.030 5.7 1.00000 0.00001 heptageniidae perlodidae
## 799 38 50 50 35 14 0.047 9.1 0.98705 0.03382 heptageniidae lepidostomatidae
## 800 38 51 50 52 25 0.071 13.5 0.99999 0.00004 heptageniidae limnephilidae
## 803 38 54 50 74 25 0.100 19.3 0.98174 0.03940 heptageniidae philopotamidae
## 806 38 57 50 27 14 0.037 7.0 0.99960 0.00176 heptageniidae rhyacophilidae
## 807 38 58 50 54 30 0.073 14.1 1.00000 0.00000 heptageniidae uenoidae
## 808 39 40 12 73 11 0.024 4.6 1.00000 0.00012 leptohyphidae leptophlebiidae
## 809 39 42 12 22 7 0.007 1.4 1.00000 0.00005 leptohyphidae chloroperlidae
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## 811 39 44 12 61 11 0.020 3.8 1.00000 0.00002 leptohyphidae nemouridae
## 812 39 45 12 51 11 0.017 3.2 1.00000 0.00000 leptohyphidae perlidae
## 813 39 46 12 22 4 0.007 1.4 0.99420 0.03483 leptohyphidae perlodidae
## 814 39 49 12 22 4 0.007 1.4 0.99420 0.03483 leptohyphidae hydroptilidae
## 815 39 50 12 35 5 0.011 2.2 0.99016 0.04568 leptohyphidae lepidostomatidae
## 820 39 58 12 54 11 0.018 3.4 1.00000 0.00000 leptohyphidae uenoidae
## 821 40 42 73 22 18 0.044 8.4 1.00000 0.00001 leptophlebiidae chloroperlidae
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## 823 40 44 73 61 47 0.121 23.2 1.00000 0.00000 leptophlebiidae nemouridae
## 824 40 45 73 51 42 0.101 19.4 1.00000 0.00000 leptophlebiidae perlidae
## 825 40 46 73 22 17 0.044 8.4 0.99999 0.00009 leptophlebiidae perlodidae
## 828 40 50 73 35 30 0.069 13.3 1.00000 0.00000 leptophlebiidae lepidostomatidae
## 829 40 51 73 52 35 0.103 19.8 1.00000 0.00000 leptophlebiidae limnephilidae
## 830 40 52 73 4 4 0.008 1.5 1.00000 0.01984 leptophlebiidae molannidae
## 831 40 53 73 6 6 0.012 2.3 1.00000 0.00265 leptophlebiidae odontoceridae
## 832 40 54 73 74 36 0.147 28.1 0.99459 0.01242 leptophlebiidae philopotamidae
## 834 40 56 73 81 41 0.160 30.8 0.99936 0.00175 leptophlebiidae polycentropodidae
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## 837 42 43 22 93 21 0.056 10.7 1.00000 0.00000 chloroperlidae leuctridae
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## 848 42 58 22 54 14 0.032 6.2 0.99996 0.00023 chloroperlidae uenoidae
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## 855 43 51 93 52 38 0.131 25.2 0.99999 0.00003 leuctridae limnephilidae
## 857 43 53 93 6 6 0.015 2.9 1.00000 0.01185 leuctridae odontoceridae
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## 875 45 46 51 22 14 0.030 5.8 0.99998 0.00011 perlidae perlodidae
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## 887 46 50 22 35 10 0.021 4.0 0.99972 0.00156 perlodidae lepidostomatidae
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## 892 46 57 22 27 7 0.016 3.1 0.99548 0.01933 perlodidae rhyacophilidae
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## 900 49 56 22 81 17 0.048 9.3 0.99993 0.00045 hydroptilidae polycentropodidae
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## 909 50 58 35 54 20 0.051 9.8 0.99999 0.00006 lepidostomatidae uenoidae
## 910 51 52 52 4 4 0.006 1.1 1.00000 0.00493 limnephilidae molannidae
## 911 51 53 52 6 6 0.008 1.6 1.00000 0.00032 limnephilidae odontoceridae
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## 920 53 54 6 74 6 0.012 2.3 1.00000 0.00288 odontoceridae philopotamidae
## 924 54 56 74 81 44 0.163 31.2 0.99997 0.00011 philopotamidae polycentropodidae
## 925 54 57 74 27 20 0.054 10.4 0.99999 0.00006 philopotamidae rhyacophilidae
## 926 54 58 74 54 30 0.108 20.8 0.99924 0.00223 philopotamidae uenoidae
## 929 55 58 9 54 7 0.013 2.5 0.99982 0.00226 phryganeidae uenoidae
## 930 56 57 81 27 18 0.059 11.4 0.99856 0.00528 polycentropodidae rhyacophilidae
## 931 56 58 81 54 30 0.119 22.8 0.99380 0.01474 polycentropodidae uenoidae
## 932 57 58 27 54 20 0.040 7.6 1.00000 0.00000 rhyacophilidae uenoidaeco[, "sp1_name"] == rownames(streambioticsum_suff_biotic_bin_t_3)[co$sp1]## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [120] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [358] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEco[, "sp2_name"] == rownames(streambioticsum_suff_biotic_bin_t_3)[co$sp2]## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [120] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [239] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [358] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEnodes <- data.frame(id = 1:nrow(streambioticsum_suff_biotic_bin_t_3),
label = rownames(streambioticsum_suff_biotic_bin_t_3),
shadow = TRUE)
edges <- data.frame(from = co$sp1, to = co$sp2,
color = ifelse(co$p_lt <= 0.05, "#B0B2C1", "#3C3F51"),
dashes = ifelse(co$p_lt <= 0.05, TRUE, FALSE))
cooc<-cooccur(streambioticsum_suff_biotic_bin_t_3, spp_names = TRUE)##
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|==============================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================| 100%visNetwork(nodes = nodes, edges = edges) %>%
visIgraphLayout(layout = "layout_with_kk")plot(cooc)
pair.attributes(cooc)## pos neg rand num_pos num_neg num_rand sppname
## 1 14.29 16.67 69.05 6 7 29 streambioticsum_suff_mtdusky
## 9 34.04 2.13 63.83 16 1 30 streambioticsum_suff_nodusky
## 19 49.02 0.00 50.98 25 0 26 streambioticsum_suff_twolined
## 30 29.63 3.70 66.67 8 1 18 streambioticsum_suff_longtail
## 39 38.24 14.71 47.06 13 5 16 streambioticsum_suff_red
## 49 0.00 0.00 100.00 0 0 5 streambioticsum_suff_mole
## 50 46.81 4.26 48.94 22 2 23 streambioticsum_suff_redback
## 51 51.72 0.00 48.28 15 0 14 streambioticsum_suff_slimy
## 52 47.22 2.78 50.00 17 1 18 blacknose_dace
## 2 0.00 0.00 100.00 0 0 5 bluegill_sunfish
## 3 0.00 0.00 100.00 0 0 23 central_stoneroller_minnow
## 4 42.86 9.52 47.62 18 4 20 creek_chub
## 5 0.00 0.00 100.00 0 0 5 green_sunfish
## 6 11.54 0.00 88.46 3 0 23 johnny_darter
## 7 3.85 0.00 96.15 1 0 25 rainbow_darter
## 8 0.00 0.00 100.00 0 0 5 redbelly_dace
## 10 4.35 0.00 95.65 1 0 22 white_sucker
## 11 3.85 0.00 96.15 1 0 25 sessile_animals
## 12 13.33 24.44 62.22 6 11 28 sow_bugs
## 13 6.67 17.78 75.56 3 8 34 scuds
## 14 17.02 0.00 82.98 8 0 39 water_mites
## 15 17.65 5.88 76.47 9 3 39 damselfly_nymphs
## 16 51.06 2.13 46.81 24 1 22 alderfly_larvae
## 17 47.06 0.00 52.94 24 0 27 dragonfly_nymphs
## 18 45.10 3.92 50.98 23 2 26 riffle_beetles
## 20 15.69 17.65 66.67 8 9 34 snails
## 21 17.78 0.00 82.22 8 0 37 clams
## 22 59.57 6.38 34.04 28 3 16 fishfly_larvae
## 23 61.90 0.00 38.10 26 0 16 water_penny_beetles
## 24 51.06 2.13 46.81 24 1 22 ameletidae
## 25 18.18 0.00 81.82 6 0 27 caenidae
## 26 3.85 0.00 96.15 1 0 25 ephemerellidae
## 27 13.33 0.00 86.67 2 0 13 ephemeridae
## 28 48.89 4.44 46.67 22 2 21 heptageniidae
## 29 48.48 3.03 48.48 16 1 16 leptohyphidae
## 31 57.45 8.51 34.04 27 4 16 leptophlebiidae
## 32 60.00 0.00 40.00 21 0 14 chloroperlidae
## 33 59.57 6.38 34.04 28 3 16 leuctridae
## 34 55.56 6.67 37.78 25 3 17 nemouridae
## 35 51.11 6.67 42.22 23 3 19 perlidae
## 36 45.71 5.71 48.57 16 2 17 perlodidae
## 37 6.67 0.00 93.33 1 0 14 glossosomatidae
## 38 17.14 0.00 82.86 6 0 29 hydroptilidae
## 40 52.63 15.79 31.58 20 6 12 lepidostomatidae
## 41 48.89 6.67 44.44 22 3 20 limnephilidae
## 42 13.04 0.00 86.96 3 0 20 molannidae
## 43 33.33 3.70 62.96 9 1 17 odontoceridae
## 44 55.32 0.00 44.68 26 0 21 philopotamidae
## 45 9.38 0.00 90.62 3 0 29 phryganeidae
## 46 34.04 2.13 63.83 16 1 30 polycentropodidae
## 47 58.33 2.78 38.89 21 1 14 rhyacophilidae
## 48 60.00 2.22 37.78 27 1 17 uenoidaepair.profile(cooc)
prob.table(cooc)## sp1 sp2 sp1_inc sp2_inc obs_cooccur prob_cooccur exp_cooccur p_lt p_gt sp1_name sp2_name
## 1 1 2 40 74 34 0.080 15.4 1.00000 0.00000 streambioticsum_suff_mtdusky streambioticsum_suff_nodusky
## 2 1 3 40 133 36 0.144 27.7 0.99988 0.00069 streambioticsum_suff_mtdusky streambioticsum_suff_twolined
## 3 1 4 40 6 0 0.007 1.2 0.24106 1.00000 streambioticsum_suff_mtdusky streambioticsum_suff_longtail
## 4 1 5 40 18 1 0.020 3.8 0.07543 0.98809 streambioticsum_suff_mtdusky streambioticsum_suff_red
## 5 1 7 40 70 9 0.076 14.6 0.02800 0.98941 streambioticsum_suff_mtdusky streambioticsum_suff_redback
## 6 1 8 40 8 0 0.009 1.7 0.14831 1.00000 streambioticsum_suff_mtdusky streambioticsum_suff_slimy
## 7 1 9 40 25 4 0.027 5.2 0.36752 0.81440 streambioticsum_suff_mtdusky blacknose_dace
## 8 1 13 40 43 3 0.047 9.0 0.00650 0.99871 streambioticsum_suff_mtdusky creek_chub
## 9 1 15 40 5 1 0.005 1.0 0.72106 0.69334 streambioticsum_suff_mtdusky johnny_darter
## 10 1 16 40 5 1 0.005 1.0 0.72106 0.69334 streambioticsum_suff_mtdusky rainbow_darter
## 11 1 22 40 5 3 0.005 1.0 0.99295 0.06201 streambioticsum_suff_mtdusky sessile_animals
## 12 1 23 40 53 15 0.058 11.0 0.95940 0.08651 streambioticsum_suff_mtdusky sow_bugs
## 13 1 24 40 60 14 0.065 12.5 0.78001 0.34645 streambioticsum_suff_mtdusky scuds
## 14 1 25 40 65 13 0.071 13.5 0.49854 0.64818 streambioticsum_suff_mtdusky water_mites
## 15 1 26 40 97 21 0.105 20.2 0.67663 0.45892 streambioticsum_suff_mtdusky damselfly_nymphs
## 16 1 27 40 79 24 0.086 16.5 0.99806 0.00574 streambioticsum_suff_mtdusky alderfly_larvae
## 17 1 28 40 125 25 0.136 26.0 0.41594 0.71959 streambioticsum_suff_mtdusky dragonfly_nymphs
## 18 1 29 40 101 15 0.110 21.0 0.02416 0.99020 streambioticsum_suff_mtdusky riffle_beetles
## 19 1 30 40 131 27 0.142 27.3 0.52620 0.62330 streambioticsum_suff_mtdusky snails
## 20 1 31 40 55 9 0.060 11.5 0.22295 0.87897 streambioticsum_suff_mtdusky clams
## 21 1 32 40 68 20 0.074 14.2 0.98988 0.02500 streambioticsum_suff_mtdusky fishfly_larvae
## 22 1 33 40 47 12 0.051 9.8 0.86771 0.23700 streambioticsum_suff_mtdusky water_penny_beetles
## 23 1 34 40 70 19 0.076 14.6 0.96403 0.07521 streambioticsum_suff_mtdusky ameletidae
## 24 1 35 40 13 3 0.014 2.7 0.72574 0.53359 streambioticsum_suff_mtdusky caenidae
## 25 1 36 40 5 2 0.005 1.0 0.93799 0.27894 streambioticsum_suff_mtdusky ephemerellidae
## 26 1 38 40 50 4 0.054 10.4 0.00566 0.99872 streambioticsum_suff_mtdusky heptageniidae
## 27 1 39 40 12 1 0.013 2.5 0.24279 0.94487 streambioticsum_suff_mtdusky leptohyphidae
## 28 1 40 40 73 15 0.079 15.2 0.54584 0.59903 streambioticsum_suff_mtdusky leptophlebiidae
## 29 1 42 40 22 4 0.024 4.6 0.49876 0.71706 streambioticsum_suff_mtdusky chloroperlidae
## 30 1 43 40 93 29 0.101 19.4 0.99987 0.00052 streambioticsum_suff_mtdusky leuctridae
## 31 1 44 40 61 10 0.066 12.7 0.20091 0.89118 streambioticsum_suff_mtdusky nemouridae
## 32 1 45 40 51 5 0.055 10.6 0.01590 0.99545 streambioticsum_suff_mtdusky perlidae
## 33 1 46 40 22 1 0.024 4.6 0.03199 0.99585 streambioticsum_suff_mtdusky perlodidae
## 34 1 49 40 22 5 0.024 4.6 0.70596 0.50124 streambioticsum_suff_mtdusky hydroptilidae
## 35 1 50 40 35 5 0.038 7.3 0.20769 0.90478 streambioticsum_suff_mtdusky lepidostomatidae
## 36 1 51 40 52 1 0.056 10.8 0.00001 1.00000 streambioticsum_suff_mtdusky limnephilidae
## 37 1 53 40 6 0 0.007 1.2 0.24106 1.00000 streambioticsum_suff_mtdusky odontoceridae
## 38 1 54 40 74 19 0.080 15.4 0.93106 0.13047 streambioticsum_suff_mtdusky philopotamidae
## 39 1 55 40 9 1 0.010 1.9 0.40623 0.88393 streambioticsum_suff_mtdusky phryganeidae
## 40 1 56 40 81 26 0.088 16.9 0.99972 0.00099 streambioticsum_suff_mtdusky polycentropodidae
## 41 1 57 40 27 4 0.029 5.6 0.29173 0.86331 streambioticsum_suff_mtdusky rhyacophilidae
## 42 1 58 40 54 11 0.059 11.2 0.54599 0.61035 streambioticsum_suff_mtdusky uenoidae
## 43 2 3 74 133 64 0.267 51.3 1.00000 0.00002 streambioticsum_suff_nodusky streambioticsum_suff_twolined
## 44 2 4 74 6 2 0.012 2.3 0.57494 0.74740 streambioticsum_suff_nodusky streambioticsum_suff_longtail
## 45 2 5 74 18 12 0.036 6.9 0.99741 0.01097 streambioticsum_suff_nodusky streambioticsum_suff_red
## 46 2 7 74 70 32 0.141 27.0 0.95515 0.08216 streambioticsum_suff_nodusky streambioticsum_suff_redback
## 47 2 8 74 8 7 0.016 3.1 0.99962 0.00576 streambioticsum_suff_nodusky streambioticsum_suff_slimy
## 48 2 9 74 25 9 0.050 9.6 0.48132 0.68777 streambioticsum_suff_nodusky blacknose_dace
## 49 2 11 74 4 1 0.008 1.5 0.49982 0.86015 streambioticsum_suff_nodusky central_stoneroller_minnow
## 50 2 13 74 43 11 0.086 16.6 0.03378 0.98613 streambioticsum_suff_nodusky creek_chub
## 51 2 15 74 5 1 0.010 1.9 0.36005 0.91520 streambioticsum_suff_nodusky johnny_darter
## 52 2 16 74 5 0 0.010 1.9 0.08480 1.00000 streambioticsum_suff_nodusky rainbow_darter
## 53 2 20 74 4 1 0.008 1.5 0.49982 0.86015 streambioticsum_suff_nodusky white_sucker
## 54 2 22 74 5 4 0.010 1.9 0.99219 0.07362 streambioticsum_suff_nodusky sessile_animals
## 55 2 23 74 53 20 0.106 20.4 0.51193 0.61868 streambioticsum_suff_nodusky sow_bugs
## 56 2 24 74 60 22 0.120 23.1 0.42257 0.69712 streambioticsum_suff_nodusky scuds
## 57 2 25 74 65 29 0.130 25.1 0.91790 0.14009 streambioticsum_suff_nodusky water_mites
## 58 2 26 74 97 33 0.195 37.4 0.12456 0.92642 streambioticsum_suff_nodusky damselfly_nymphs
## 59 2 27 74 79 42 0.159 30.4 0.99986 0.00044 streambioticsum_suff_nodusky alderfly_larvae
## 60 2 28 74 125 53 0.251 48.2 0.95199 0.08874 streambioticsum_suff_nodusky dragonfly_nymphs
## 61 2 29 74 101 34 0.203 38.9 0.09429 0.94653 streambioticsum_suff_nodusky riffle_beetles
## 62 2 30 74 131 45 0.263 50.5 0.05656 0.97128 streambioticsum_suff_nodusky snails
## 63 2 31 74 55 24 0.110 21.2 0.86045 0.22450 streambioticsum_suff_nodusky clams
## 64 2 32 74 68 39 0.137 26.2 0.99998 0.00007 streambioticsum_suff_nodusky fishfly_larvae
## 65 2 33 74 47 26 0.094 18.1 0.99792 0.00577 streambioticsum_suff_nodusky water_penny_beetles
## 66 2 34 74 70 34 0.141 27.0 0.98955 0.02257 streambioticsum_suff_nodusky ameletidae
## 67 2 35 74 13 5 0.026 5.0 0.62010 0.61060 streambioticsum_suff_nodusky caenidae
## 68 2 36 74 5 1 0.010 1.9 0.36005 0.91520 streambioticsum_suff_nodusky ephemerellidae
## 69 2 37 74 3 1 0.006 1.2 0.66972 0.77015 streambioticsum_suff_nodusky ephemeridae
## 70 2 38 74 50 20 0.100 19.3 0.66280 0.46670 streambioticsum_suff_nodusky heptageniidae
## 71 2 39 74 12 6 0.024 4.6 0.87382 0.29165 streambioticsum_suff_nodusky leptohyphidae
## 72 2 40 74 73 39 0.147 28.1 0.99973 0.00079 streambioticsum_suff_nodusky leptophlebiidae
## 73 2 42 74 22 9 0.044 8.5 0.68612 0.49055 streambioticsum_suff_nodusky chloroperlidae
## 74 2 43 74 93 48 0.187 35.8 0.99992 0.00026 streambioticsum_suff_nodusky leuctridae
## 75 2 44 74 61 32 0.122 23.5 0.99779 0.00568 streambioticsum_suff_nodusky nemouridae
## 76 2 45 74 51 20 0.102 19.7 0.61355 0.51848 streambioticsum_suff_nodusky perlidae
## 77 2 46 74 22 10 0.044 8.5 0.82697 0.31388 streambioticsum_suff_nodusky perlodidae
## 78 2 47 74 3 3 0.006 1.2 1.00000 0.05582 streambioticsum_suff_nodusky glossosomatidae
## 79 2 49 74 22 9 0.044 8.5 0.68612 0.49055 streambioticsum_suff_nodusky hydroptilidae
## 80 2 50 74 35 18 0.070 13.5 0.97182 0.06285 streambioticsum_suff_nodusky lepidostomatidae
## 81 2 51 74 52 19 0.104 20.0 0.43060 0.69485 streambioticsum_suff_nodusky limnephilidae
## 82 2 52 74 4 2 0.008 1.5 0.83962 0.50018 streambioticsum_suff_nodusky molannidae
## 83 2 53 74 6 5 0.012 2.3 0.99712 0.03244 streambioticsum_suff_nodusky odontoceridae
## 84 2 54 74 74 37 0.149 28.5 0.99683 0.00765 streambioticsum_suff_nodusky philopotamidae
## 85 2 55 74 9 5 0.018 3.5 0.92107 0.23196 streambioticsum_suff_nodusky phryganeidae
## 86 2 56 74 81 42 0.163 31.2 0.99965 0.00101 streambioticsum_suff_nodusky polycentropodidae
## 87 2 57 74 27 19 0.054 10.4 0.99994 0.00032 streambioticsum_suff_nodusky rhyacophilidae
## 88 2 58 74 54 33 0.108 20.8 0.99998 0.00007 streambioticsum_suff_nodusky uenoidae
## 89 3 4 133 6 5 0.022 4.2 0.89337 0.40154 streambioticsum_suff_twolined streambioticsum_suff_longtail
## 90 3 5 133 18 17 0.065 12.5 0.99908 0.00935 streambioticsum_suff_twolined streambioticsum_suff_red
## 91 3 6 133 2 2 0.007 1.4 1.00000 0.47873 streambioticsum_suff_twolined streambioticsum_suff_mole
## 92 3 7 133 70 57 0.253 48.5 0.99864 0.00399 streambioticsum_suff_twolined streambioticsum_suff_redback
## 93 3 8 133 8 8 0.029 5.5 1.00000 0.04959 streambioticsum_suff_twolined streambioticsum_suff_slimy
## 94 3 9 133 25 22 0.090 17.3 0.99524 0.02079 streambioticsum_suff_twolined blacknose_dace
## 95 3 10 133 2 2 0.007 1.4 1.00000 0.47873 streambioticsum_suff_twolined bluegill_sunfish
## 96 3 11 133 4 2 0.014 2.8 0.36081 0.91291 streambioticsum_suff_twolined central_stoneroller_minnow
## 97 3 13 133 43 38 0.155 29.8 0.99976 0.00113 streambioticsum_suff_twolined creek_chub
## 98 3 14 133 2 2 0.007 1.4 1.00000 0.47873 streambioticsum_suff_twolined green_sunfish
## 99 3 15 133 5 4 0.018 3.5 0.84422 0.51203 streambioticsum_suff_twolined johnny_darter
## 100 3 16 133 5 5 0.018 3.5 1.00000 0.15578 streambioticsum_suff_twolined rainbow_darter
## 101 3 18 133 2 2 0.007 1.4 1.00000 0.47873 streambioticsum_suff_twolined redbelly_dace
## 102 3 20 133 4 3 0.014 2.8 0.77297 0.63919 streambioticsum_suff_twolined white_sucker
## 103 3 22 133 5 5 0.018 3.5 1.00000 0.15578 streambioticsum_suff_twolined sessile_animals
## 104 3 23 133 53 33 0.191 36.7 0.13085 0.92853 streambioticsum_suff_twolined sow_bugs
## 105 3 24 133 60 40 0.216 41.6 0.35741 0.75802 streambioticsum_suff_twolined scuds
## 106 3 25 133 65 49 0.235 45.0 0.93177 0.12489 streambioticsum_suff_twolined water_mites
## 107 3 26 133 97 72 0.350 67.2 0.95176 0.08880 streambioticsum_suff_twolined damselfly_nymphs
## 108 3 27 133 79 68 0.285 54.7 1.00000 0.00001 streambioticsum_suff_twolined alderfly_larvae
## 109 3 28 133 125 98 0.451 86.6 0.99994 0.00020 streambioticsum_suff_twolined dragonfly_nymphs
## 110 3 29 133 101 82 0.364 70.0 0.99996 0.00014 streambioticsum_suff_twolined riffle_beetles
## 111 3 30 133 131 91 0.473 90.7 0.60292 0.52959 streambioticsum_suff_twolined snails
## 112 3 31 133 55 39 0.198 38.1 0.68328 0.44862 streambioticsum_suff_twolined clams
## 113 3 32 133 68 60 0.245 47.1 1.00000 0.00001 streambioticsum_suff_twolined fishfly_larvae
## 114 3 33 133 47 45 0.170 32.6 1.00000 0.00000 streambioticsum_suff_twolined water_penny_beetles
## 115 3 34 133 70 61 0.253 48.5 1.00000 0.00002 streambioticsum_suff_twolined ameletidae
## 116 3 35 133 13 11 0.047 9.0 0.94870 0.17768 streambioticsum_suff_twolined caenidae
## 117 3 36 133 5 5 0.018 3.5 1.00000 0.15578 streambioticsum_suff_twolined ephemerellidae
## 118 3 37 133 3 2 0.011 2.1 0.66993 0.77605 streambioticsum_suff_twolined ephemeridae
## 119 3 38 133 50 42 0.180 34.6 0.99825 0.00576 streambioticsum_suff_twolined heptageniidae
## 120 3 39 133 12 10 0.043 8.3 0.92926 0.22724 streambioticsum_suff_twolined leptohyphidae
## 121 3 40 133 73 68 0.263 50.6 1.00000 0.00000 streambioticsum_suff_twolined leptophlebiidae
## 122 3 42 133 22 20 0.079 15.2 0.99783 0.01294 streambioticsum_suff_twolined chloroperlidae
## 123 3 43 133 93 82 0.336 64.4 1.00000 0.00000 streambioticsum_suff_twolined leuctridae
## 124 3 44 133 61 53 0.220 42.3 0.99996 0.00017 streambioticsum_suff_twolined nemouridae
## 125 3 45 133 51 47 0.184 35.3 1.00000 0.00001 streambioticsum_suff_twolined perlidae
## 126 3 46 133 22 17 0.079 15.2 0.86809 0.27367 streambioticsum_suff_twolined perlodidae
## 127 3 47 133 3 2 0.011 2.1 0.66993 0.77605 streambioticsum_suff_twolined glossosomatidae
## 128 3 49 133 22 15 0.079 15.2 0.54094 0.64938 streambioticsum_suff_twolined hydroptilidae
## 129 3 50 133 35 32 0.126 24.2 0.99988 0.00078 streambioticsum_suff_twolined lepidostomatidae
## 130 3 51 133 52 43 0.188 36.0 0.99678 0.00959 streambioticsum_suff_twolined limnephilidae
## 131 3 52 133 4 4 0.014 2.8 1.00000 0.22703 streambioticsum_suff_twolined molannidae
## 132 3 53 133 6 6 0.022 4.2 1.00000 0.10663 streambioticsum_suff_twolined odontoceridae
## 133 3 54 133 74 68 0.267 51.3 1.00000 0.00000 streambioticsum_suff_twolined philopotamidae
## 134 3 55 133 9 6 0.032 6.2 0.55971 0.71623 streambioticsum_suff_twolined phryganeidae
## 135 3 56 133 81 70 0.292 56.1 1.00000 0.00001 streambioticsum_suff_twolined polycentropodidae
## 136 3 57 133 27 25 0.097 18.7 0.99969 0.00238 streambioticsum_suff_twolined rhyacophilidae
## 137 3 58 133 54 48 0.195 37.4 0.99998 0.00011 streambioticsum_suff_twolined uenoidae
## 138 4 7 6 70 6 0.011 2.2 1.00000 0.00204 streambioticsum_suff_longtail streambioticsum_suff_redback
## 139 4 13 6 43 1 0.007 1.3 0.59589 0.78655 streambioticsum_suff_longtail creek_chub
## 140 4 23 6 53 0 0.009 1.7 0.13965 1.00000 streambioticsum_suff_longtail sow_bugs
## 141 4 24 6 60 0 0.010 1.9 0.10182 1.00000 streambioticsum_suff_longtail scuds
## 142 4 25 6 65 1 0.011 2.0 0.33739 0.91961 streambioticsum_suff_longtail water_mites
## 143 4 26 6 97 3 0.016 3.0 0.64882 0.66856 streambioticsum_suff_longtail damselfly_nymphs
## 144 4 27 6 79 5 0.013 2.5 0.99568 0.04393 streambioticsum_suff_longtail alderfly_larvae
## 145 4 28 6 125 4 0.020 3.9 0.68268 0.65056 streambioticsum_suff_longtail dragonfly_nymphs
## 146 4 29 6 101 3 0.016 3.2 0.60817 0.70668 streambioticsum_suff_longtail riffle_beetles
## 147 4 30 6 131 1 0.021 4.1 0.01298 0.99914 streambioticsum_suff_longtail snails
## 148 4 31 6 55 2 0.009 1.7 0.77168 0.55266 streambioticsum_suff_longtail clams
## 149 4 32 6 68 5 0.011 2.1 0.99830 0.02180 streambioticsum_suff_longtail fishfly_larvae
## 150 4 33 6 47 4 0.008 1.5 0.99637 0.03259 streambioticsum_suff_longtail water_penny_beetles
## 151 4 34 6 70 2 0.011 2.2 0.61841 0.71148 streambioticsum_suff_longtail ameletidae
## 152 4 38 6 50 3 0.008 1.6 0.95922 0.18319 streambioticsum_suff_longtail heptageniidae
## 153 4 40 6 73 5 0.012 2.3 0.99735 0.03045 streambioticsum_suff_longtail leptophlebiidae
## 154 4 43 6 93 5 0.015 2.9 0.98815 0.09187 streambioticsum_suff_longtail leuctridae
## 155 4 44 6 61 5 0.010 1.9 0.99914 0.01298 streambioticsum_suff_longtail nemouridae
## 156 4 45 6 51 3 0.008 1.6 0.95621 0.19190 streambioticsum_suff_longtail perlidae
## 157 4 50 6 35 4 0.006 1.1 0.99918 0.01079 streambioticsum_suff_longtail lepidostomatidae
## 158 4 51 6 52 5 0.008 1.6 0.99968 0.00598 streambioticsum_suff_longtail limnephilidae
## 159 4 54 6 74 2 0.012 2.3 0.57494 0.74740 streambioticsum_suff_longtail philopotamidae
## 160 4 56 6 81 2 0.013 2.5 0.49844 0.80334 streambioticsum_suff_longtail polycentropodidae
## 161 4 58 6 54 4 0.009 1.7 0.99281 0.05368 streambioticsum_suff_longtail uenoidae
## 162 5 7 18 70 17 0.034 6.6 1.00000 0.00000 streambioticsum_suff_red streambioticsum_suff_redback
## 163 5 9 18 25 0 0.012 2.3 0.07145 1.00000 streambioticsum_suff_red blacknose_dace
## 164 5 13 18 43 0 0.021 4.0 0.00814 1.00000 streambioticsum_suff_red creek_chub
## 165 5 23 18 53 1 0.026 5.0 0.01896 0.99785 streambioticsum_suff_red sow_bugs
## 166 5 24 18 60 8 0.029 5.6 0.93450 0.15803 streambioticsum_suff_red scuds
## 167 5 25 18 65 8 0.032 6.1 0.89430 0.22802 streambioticsum_suff_red water_mites
## 168 5 26 18 97 4 0.047 9.1 0.01042 0.99779 streambioticsum_suff_red damselfly_nymphs
## 169 5 27 18 79 8 0.039 7.4 0.71136 0.47635 streambioticsum_suff_red alderfly_larvae
## 170 5 28 18 125 17 0.061 11.7 0.99972 0.00338 streambioticsum_suff_red dragonfly_nymphs
## 171 5 29 18 101 6 0.049 9.5 0.07021 0.97612 streambioticsum_suff_red riffle_beetles
## 172 5 30 18 131 4 0.064 12.3 0.00003 1.00000 streambioticsum_suff_red snails
## 173 5 31 18 55 7 0.027 5.2 0.89782 0.22668 streambioticsum_suff_red clams
## 174 5 32 18 68 14 0.033 6.4 0.99998 0.00015 streambioticsum_suff_red fishfly_larvae
## 175 5 33 18 47 7 0.023 4.4 0.95746 0.11627 streambioticsum_suff_red water_penny_beetles
## 176 5 34 18 70 1 0.034 6.6 0.00226 0.99983 streambioticsum_suff_red ameletidae
## 177 5 35 18 13 0 0.006 1.2 0.26613 1.00000 streambioticsum_suff_red caenidae
## 178 5 38 18 50 8 0.024 4.7 0.98048 0.06084 streambioticsum_suff_red heptageniidae
## 179 5 39 18 12 1 0.006 1.1 0.68755 0.70430 streambioticsum_suff_red leptohyphidae
## 180 5 40 18 73 17 0.036 6.8 1.00000 0.00000 streambioticsum_suff_red leptophlebiidae
## 181 5 42 18 22 4 0.011 2.1 0.96122 0.13324 streambioticsum_suff_red chloroperlidae
## 182 5 43 18 93 17 0.045 8.7 1.00000 0.00002 streambioticsum_suff_red leuctridae
## 183 5 44 18 61 15 0.030 5.7 1.00000 0.00000 streambioticsum_suff_red nemouridae
## 184 5 45 18 51 8 0.025 4.8 0.97757 0.06799 streambioticsum_suff_red perlidae
## 185 5 46 18 22 3 0.011 2.1 0.86676 0.34004 streambioticsum_suff_red perlodidae
## 186 5 49 18 22 2 0.011 2.1 0.65996 0.64056 streambioticsum_suff_red hydroptilidae
## 187 5 50 18 35 17 0.017 3.3 1.00000 0.00000 streambioticsum_suff_red lepidostomatidae
## 188 5 51 18 52 16 0.025 4.9 1.00000 0.00000 streambioticsum_suff_red limnephilidae
## 189 5 54 18 74 14 0.036 6.9 0.99994 0.00047 streambioticsum_suff_red philopotamidae
## 190 5 56 18 81 9 0.040 7.6 0.83054 0.32222 streambioticsum_suff_red polycentropodidae
## 191 5 57 18 27 12 0.013 2.5 1.00000 0.00000 streambioticsum_suff_red rhyacophilidae
## 192 5 58 18 54 11 0.026 5.1 0.99960 0.00219 streambioticsum_suff_red uenoidae
## 193 6 26 2 97 0 0.005 1.0 0.24351 1.00000 streambioticsum_suff_mole damselfly_nymphs
## 194 6 28 2 125 1 0.007 1.3 0.57733 0.87942 streambioticsum_suff_mole dragonfly_nymphs
## 195 6 29 2 101 2 0.005 1.1 1.00000 0.27541 streambioticsum_suff_mole riffle_beetles
## 196 6 30 2 131 0 0.007 1.4 0.09980 1.00000 streambioticsum_suff_mole snails
## 197 7 8 70 8 7 0.015 2.9 0.99976 0.00394 streambioticsum_suff_redback streambioticsum_suff_slimy
## 198 7 9 70 25 14 0.047 9.1 0.99086 0.02696 streambioticsum_suff_redback blacknose_dace
## 199 7 11 70 4 2 0.008 1.5 0.86158 0.46328 streambioticsum_suff_redback central_stoneroller_minnow
## 200 7 13 70 43 16 0.082 15.7 0.61939 0.52175 streambioticsum_suff_redback creek_chub
## 201 7 15 70 5 1 0.009 1.8 0.39848 0.89953 streambioticsum_suff_redback johnny_darter
## 202 7 16 70 5 2 0.009 1.8 0.74408 0.60152 streambioticsum_suff_redback rainbow_darter
## 203 7 20 70 4 0 0.008 1.5 0.16007 1.00000 streambioticsum_suff_redback white_sucker
## 204 7 22 70 5 1 0.009 1.8 0.39848 0.89953 streambioticsum_suff_redback sessile_animals
## 205 7 23 70 53 15 0.101 19.3 0.09894 0.94865 streambioticsum_suff_redback sow_bugs
## 206 7 24 70 60 23 0.114 21.9 0.70172 0.41788 streambioticsum_suff_redback scuds
## 207 7 25 70 65 25 0.123 23.7 0.71697 0.39808 streambioticsum_suff_redback water_mites
## 208 7 26 70 97 31 0.184 35.4 0.12320 0.92781 streambioticsum_suff_redback damselfly_nymphs
## 209 7 27 70 79 39 0.150 28.8 0.99943 0.00159 streambioticsum_suff_redback alderfly_larvae
## 210 7 28 70 125 50 0.237 45.6 0.94036 0.10783 streambioticsum_suff_redback dragonfly_nymphs
## 211 7 29 70 101 37 0.192 36.8 0.58025 0.53888 streambioticsum_suff_redback riffle_beetles
## 212 7 30 70 131 36 0.249 47.8 0.00016 0.99996 streambioticsum_suff_redback snails
## 213 7 31 70 55 20 0.104 20.1 0.56148 0.57011 streambioticsum_suff_redback clams
## 214 7 32 70 68 38 0.129 24.8 0.99999 0.00004 streambioticsum_suff_redback fishfly_larvae
## 215 7 33 70 47 26 0.089 17.1 0.99937 0.00197 streambioticsum_suff_redback water_penny_beetles
## 216 7 34 70 70 24 0.133 25.5 0.37663 0.73464 streambioticsum_suff_redback ameletidae
## 217 7 35 70 13 4 0.025 4.7 0.45292 0.76602 streambioticsum_suff_redback caenidae
## 218 7 36 70 5 1 0.009 1.8 0.39848 0.89953 streambioticsum_suff_redback ephemerellidae
## 219 7 37 70 3 3 0.006 1.1 1.00000 0.04714 streambioticsum_suff_redback ephemeridae
## 220 7 38 70 50 28 0.095 18.2 0.99974 0.00087 streambioticsum_suff_redback heptageniidae
## 221 7 39 70 12 7 0.023 4.4 0.97145 0.09591 streambioticsum_suff_redback leptohyphidae
## 222 7 40 70 73 42 0.139 26.6 1.00000 0.00000 streambioticsum_suff_redback leptophlebiidae
## 223 7 42 70 22 14 0.042 8.0 0.99862 0.00566 streambioticsum_suff_redback chloroperlidae
## 224 7 43 70 93 45 0.177 33.9 0.99976 0.00071 streambioticsum_suff_redback leuctridae
## 225 7 44 70 61 35 0.116 22.2 0.99999 0.00004 streambioticsum_suff_redback nemouridae
## 226 7 45 70 51 32 0.097 18.6 1.00000 0.00001 streambioticsum_suff_redback perlidae
## 227 7 46 70 22 12 0.042 8.0 0.98104 0.05266 streambioticsum_suff_redback perlodidae
## 228 7 47 70 3 2 0.006 1.1 0.95286 0.30085 streambioticsum_suff_redback glossosomatidae
## 229 7 49 70 22 7 0.042 8.0 0.40961 0.75999 streambioticsum_suff_redback hydroptilidae
## 230 7 50 70 35 25 0.066 12.8 1.00000 0.00000 streambioticsum_suff_redback lepidostomatidae
## 231 7 51 70 52 33 0.099 19.0 1.00000 0.00000 streambioticsum_suff_redback limnephilidae
## 232 7 52 70 4 4 0.008 1.5 1.00000 0.01671 streambioticsum_suff_redback molannidae
## 233 7 53 70 6 6 0.011 2.2 1.00000 0.00204 streambioticsum_suff_redback odontoceridae
## 234 7 54 70 74 34 0.141 27.0 0.98955 0.02257 streambioticsum_suff_redback philopotamidae
## 235 7 55 70 9 6 0.017 3.3 0.98754 0.06038 streambioticsum_suff_redback phryganeidae
## 236 7 56 70 81 29 0.154 29.5 0.49708 0.62209 streambioticsum_suff_redback polycentropodidae
## 237 7 57 70 27 17 0.051 9.8 0.99941 0.00240 streambioticsum_suff_redback rhyacophilidae
## 238 7 58 70 54 35 0.103 19.7 1.00000 0.00000 streambioticsum_suff_redback uenoidae
## 239 8 9 8 25 4 0.005 1.0 0.99891 0.01110 streambioticsum_suff_slimy blacknose_dace
## 240 8 13 8 43 2 0.009 1.8 0.74479 0.56889 streambioticsum_suff_slimy creek_chub
## 241 8 23 8 53 1 0.012 2.2 0.30011 0.92875 streambioticsum_suff_slimy sow_bugs
## 242 8 24 8 60 4 0.013 2.5 0.93590 0.21322 streambioticsum_suff_slimy scuds
## 243 8 25 8 65 6 0.014 2.7 0.99763 0.01909 streambioticsum_suff_slimy water_mites
## 244 8 26 8 97 2 0.021 4.0 0.13262 0.96980 streambioticsum_suff_slimy damselfly_nymphs
## 245 8 27 8 79 4 0.017 3.3 0.81298 0.43188 streambioticsum_suff_slimy alderfly_larvae
## 246 8 28 8 125 7 0.027 5.2 0.97023 0.16499 streambioticsum_suff_slimy dragonfly_nymphs
## 247 8 29 8 101 7 0.022 4.2 0.99489 0.04473 streambioticsum_suff_slimy riffle_beetles
## 248 8 30 8 131 5 0.028 5.5 0.49446 0.77663 streambioticsum_suff_slimy snails
## 249 8 31 8 55 3 0.012 2.3 0.83429 0.41376 streambioticsum_suff_slimy clams
## 250 8 32 8 68 6 0.015 2.8 0.99677 0.02436 streambioticsum_suff_slimy fishfly_larvae
## 251 8 33 8 47 5 0.010 2.0 0.99692 0.02240 streambioticsum_suff_slimy water_penny_beetles
## 252 8 34 8 70 4 0.015 2.9 0.88113 0.32270 streambioticsum_suff_slimy ameletidae
## 253 8 38 8 50 4 0.011 2.1 0.97055 0.12408 streambioticsum_suff_slimy heptageniidae
## 254 8 40 8 73 7 0.016 3.0 0.99966 0.00525 streambioticsum_suff_slimy leptophlebiidae
## 255 8 43 8 93 7 0.020 3.9 0.99742 0.02632 streambioticsum_suff_slimy leuctridae
## 256 8 44 8 61 6 0.013 2.5 0.99848 0.01349 streambioticsum_suff_slimy nemouridae
## 257 8 45 8 51 4 0.011 2.1 0.96788 0.13191 streambioticsum_suff_slimy perlidae
## 258 8 50 8 35 3 0.008 1.5 0.96199 0.16149 streambioticsum_suff_slimy lepidostomatidae
## 259 8 51 8 52 4 0.011 2.2 0.96505 0.13999 streambioticsum_suff_slimy limnephilidae
## 260 8 54 8 74 7 0.016 3.1 0.99962 0.00576 streambioticsum_suff_slimy philopotamidae
## 261 8 56 8 81 7 0.018 3.4 0.99919 0.01058 streambioticsum_suff_slimy polycentropodidae
## 262 8 57 8 27 4 0.006 1.1 0.99839 0.01483 streambioticsum_suff_slimy rhyacophilidae
## 263 8 58 8 54 6 0.012 2.2 0.99936 0.00682 streambioticsum_suff_slimy uenoidae
## 264 9 13 25 43 15 0.029 5.6 1.00000 0.00001 blacknose_dace creek_chub
## 265 9 23 25 53 9 0.036 6.9 0.89177 0.21839 blacknose_dace sow_bugs
## 266 9 24 25 60 7 0.041 7.8 0.45119 0.72304 blacknose_dace scuds
## 267 9 25 25 65 11 0.044 8.5 0.91375 0.17732 blacknose_dace water_mites
## 268 9 26 25 97 19 0.066 12.6 0.99872 0.00527 blacknose_dace damselfly_nymphs
## 269 9 27 25 79 14 0.054 10.3 0.96599 0.08154 blacknose_dace alderfly_larvae
## 270 9 28 25 125 20 0.085 16.3 0.97541 0.07000 blacknose_dace dragonfly_nymphs
## 271 9 29 25 101 23 0.068 13.2 1.00000 0.00001 blacknose_dace riffle_beetles
## 272 9 30 25 131 19 0.089 17.1 0.87121 0.25745 blacknose_dace snails
## 273 9 31 25 55 5 0.037 7.2 0.21855 0.90007 blacknose_dace clams
## 274 9 32 25 68 13 0.046 8.9 0.97978 0.05304 blacknose_dace fishfly_larvae
## 275 9 33 25 47 11 0.032 6.1 0.99471 0.01776 blacknose_dace water_penny_beetles
## 276 9 34 25 70 16 0.047 9.1 0.99938 0.00261 blacknose_dace ameletidae
## 277 9 35 25 13 3 0.009 1.7 0.92899 0.23008 blacknose_dace caenidae
## 278 9 38 25 50 14 0.034 6.5 0.99988 0.00061 blacknose_dace heptageniidae
## 279 9 39 25 12 5 0.008 1.6 0.99854 0.01058 blacknose_dace leptohyphidae
## 280 9 40 25 73 14 0.050 9.5 0.98533 0.04028 blacknose_dace leptophlebiidae
## 281 9 42 25 22 9 0.015 2.9 0.99995 0.00042 blacknose_dace chloroperlidae
## 282 9 43 25 93 19 0.063 12.1 0.99940 0.00272 blacknose_dace leuctridae
## 283 9 44 25 61 15 0.041 7.9 0.99961 0.00172 blacknose_dace nemouridae
## 284 9 45 25 51 15 0.035 6.6 0.99997 0.00015 blacknose_dace perlidae
## 285 9 46 25 22 8 0.015 2.9 0.99958 0.00256 blacknose_dace perlodidae
## 286 9 49 25 22 3 0.015 2.9 0.68430 0.57126 blacknose_dace hydroptilidae
## 287 9 50 25 35 0 0.024 4.6 0.00444 1.00000 blacknose_dace lepidostomatidae
## 288 9 51 25 52 6 0.035 6.8 0.45910 0.72376 blacknose_dace limnephilidae
## 289 9 54 25 74 14 0.050 9.6 0.98296 0.04570 blacknose_dace philopotamidae
## 290 9 55 25 9 1 0.006 1.2 0.66847 0.72321 blacknose_dace phryganeidae
## 291 9 56 25 81 13 0.055 10.5 0.89957 0.19769 blacknose_dace polycentropodidae
## 292 9 57 25 27 3 0.018 3.5 0.51885 0.72174 blacknose_dace rhyacophilidae
## 293 9 58 25 54 10 0.037 7.0 0.94757 0.12091 blacknose_dace uenoidae
## 294 10 26 2 97 2 0.005 1.0 1.00000 0.25393 bluegill_sunfish damselfly_nymphs
## 295 10 28 2 125 2 0.007 1.3 1.00000 0.42267 bluegill_sunfish dragonfly_nymphs
## 296 10 29 2 101 2 0.005 1.1 1.00000 0.27541 bluegill_sunfish riffle_beetles
## 297 10 30 2 131 2 0.007 1.4 1.00000 0.46439 bluegill_sunfish snails
## 298 11 23 4 53 3 0.006 1.1 0.99466 0.06468 central_stoneroller_minnow sow_bugs
## 299 11 24 4 60 2 0.007 1.2 0.90879 0.37011 central_stoneroller_minnow scuds
## 300 11 25 4 65 3 0.007 1.4 0.98766 0.11344 central_stoneroller_minnow water_mites
## 301 11 26 4 97 3 0.011 2.0 0.93685 0.31842 central_stoneroller_minnow damselfly_nymphs
## 302 11 27 4 79 3 0.009 1.6 0.97262 0.19024 central_stoneroller_minnow alderfly_larvae
## 303 11 28 4 125 3 0.014 2.6 0.82338 0.56461 central_stoneroller_minnow dragonfly_nymphs
## 304 11 29 4 101 4 0.011 2.1 1.00000 0.07441 central_stoneroller_minnow riffle_beetles
## 305 11 30 4 131 4 0.014 2.7 1.00000 0.21353 central_stoneroller_minnow snails
## 306 11 31 4 55 2 0.006 1.1 0.92828 0.32384 central_stoneroller_minnow clams
## 307 11 32 4 68 2 0.007 1.4 0.87190 0.44470 central_stoneroller_minnow fishfly_larvae
## 308 11 34 4 70 2 0.008 1.5 0.86158 0.46328 central_stoneroller_minnow ameletidae
## 309 11 38 4 50 1 0.005 1.0 0.72158 0.70413 central_stoneroller_minnow heptageniidae
## 310 11 40 4 73 0 0.008 1.5 0.14472 1.00000 central_stoneroller_minnow leptophlebiidae
## 311 11 43 4 93 1 0.010 1.9 0.33445 0.93140 central_stoneroller_minnow leuctridae
## 312 11 44 4 61 1 0.007 1.3 0.62058 0.78647 central_stoneroller_minnow nemouridae
## 313 11 45 4 51 1 0.006 1.1 0.71259 0.71246 central_stoneroller_minnow perlidae
## 314 11 51 4 52 3 0.006 1.1 0.99507 0.06132 central_stoneroller_minnow limnephilidae
## 315 11 54 4 74 1 0.008 1.5 0.49982 0.86015 central_stoneroller_minnow philopotamidae
## 316 11 56 4 81 0 0.009 1.7 0.10915 1.00000 central_stoneroller_minnow polycentropodidae
## 317 11 58 4 54 1 0.006 1.1 0.68532 0.73641 central_stoneroller_minnow uenoidae
## 318 13 15 43 5 4 0.006 1.1 0.99953 0.00938 creek_chub johnny_darter
## 319 13 16 43 5 3 0.006 1.1 0.99062 0.07533 creek_chub rainbow_darter
## 320 13 22 43 5 4 0.006 1.1 0.99953 0.00938 creek_chub sessile_animals
## 321 13 23 43 53 13 0.062 11.9 0.73906 0.39787 creek_chub sow_bugs
## 322 13 24 43 60 13 0.070 13.4 0.51451 0.63235 creek_chub scuds
## 323 13 25 43 65 16 0.076 14.6 0.76291 0.36163 creek_chub water_mites
## 324 13 26 43 97 32 0.113 21.7 0.99993 0.00030 creek_chub damselfly_nymphs
## 325 13 27 43 79 24 0.092 17.7 0.99138 0.02104 creek_chub alderfly_larvae
## 326 13 28 43 125 38 0.146 28.0 0.99998 0.00013 creek_chub dragonfly_nymphs
## 327 13 29 43 101 41 0.118 22.6 1.00000 0.00000 creek_chub riffle_beetles
## 328 13 30 43 131 39 0.153 29.3 0.99998 0.00013 creek_chub snails
## 329 13 31 43 55 18 0.064 12.3 0.98976 0.02552 creek_chub clams
## 330 13 32 43 68 18 0.079 15.2 0.88117 0.20473 creek_chub fishfly_larvae
## 331 13 33 43 47 14 0.055 10.5 0.94259 0.11682 creek_chub water_penny_beetles
## 332 13 34 43 70 26 0.082 15.7 0.99994 0.00025 creek_chub ameletidae
## 333 13 35 43 13 6 0.015 2.9 0.98948 0.04399 creek_chub caenidae
## 334 13 36 43 5 3 0.006 1.1 0.99062 0.07533 creek_chub ephemerellidae
## 335 13 38 43 50 19 0.058 11.2 0.99923 0.00260 creek_chub heptageniidae
## 336 13 39 43 12 5 0.014 2.7 0.97146 0.10170 creek_chub leptohyphidae
## 337 13 40 43 73 21 0.085 16.3 0.96593 0.07032 creek_chub leptophlebiidae
## 338 13 42 43 22 8 0.026 4.9 0.96907 0.08504 creek_chub chloroperlidae
## 339 13 43 43 93 27 0.108 20.8 0.98980 0.02450 creek_chub leuctridae
## 340 13 44 43 61 17 0.071 13.7 0.92176 0.14584 creek_chub nemouridae
## 341 13 45 43 51 22 0.059 11.4 0.99998 0.00007 creek_chub perlidae
## 342 13 46 43 22 6 0.026 4.9 0.80673 0.36474 creek_chub perlodidae
## 343 13 49 43 22 3 0.026 4.9 0.22430 0.91301 creek_chub hydroptilidae
## 344 13 50 43 35 3 0.041 7.8 0.02005 0.99532 creek_chub lepidostomatidae
## 345 13 51 43 52 18 0.061 11.6 0.99534 0.01287 creek_chub limnephilidae
## 346 13 53 43 6 0 0.007 1.3 0.21345 1.00000 creek_chub odontoceridae
## 347 13 54 43 74 23 0.086 16.6 0.99269 0.01823 creek_chub philopotamidae
## 348 13 55 43 9 5 0.010 2.0 0.99546 0.02804 creek_chub phryganeidae
## 349 13 56 43 81 17 0.094 18.1 0.41316 0.71604 creek_chub polycentropodidae
## 350 13 57 43 27 7 0.031 6.0 0.77006 0.39866 creek_chub rhyacophilidae
## 351 13 58 43 54 16 0.063 12.1 0.95292 0.09635 creek_chub uenoidae
## 352 14 26 2 97 2 0.005 1.0 1.00000 0.25393 green_sunfish damselfly_nymphs
## 353 14 28 2 125 2 0.007 1.3 1.00000 0.42267 green_sunfish dragonfly_nymphs
## 354 14 29 2 101 2 0.005 1.1 1.00000 0.27541 green_sunfish riffle_beetles
## 355 14 30 2 131 2 0.007 1.4 1.00000 0.46439 green_sunfish snails
## 356 15 23 5 53 1 0.007 1.4 0.57744 0.80511 johnny_darter sow_bugs
## 357 15 24 5 60 1 0.008 1.6 0.50132 0.85007 johnny_darter scuds
## 358 15 25 5 65 3 0.009 1.7 0.95432 0.21507 johnny_darter water_mites
## 359 15 26 5 97 3 0.013 2.5 0.80922 0.50987 johnny_darter damselfly_nymphs
## 360 15 27 5 79 2 0.011 2.1 0.66423 0.68542 johnny_darter alderfly_larvae
## 361 15 28 5 125 5 0.017 3.3 1.00000 0.11368 johnny_darter dragonfly_nymphs
## 362 15 29 5 101 5 0.014 2.6 1.00000 0.03839 johnny_darter riffle_beetles
## 363 15 30 5 131 5 0.018 3.4 1.00000 0.14425 johnny_darter snails
## 364 15 31 5 55 2 0.007 1.4 0.85720 0.44454 johnny_darter clams
## 365 15 32 5 68 2 0.009 1.8 0.76076 0.58167 johnny_darter fishfly_larvae
## 366 15 33 5 47 2 0.006 1.2 0.90467 0.35608 johnny_darter water_penny_beetles
## 367 15 34 5 70 4 0.009 1.8 0.99413 0.06009 johnny_darter ameletidae
## 368 15 38 5 50 3 0.007 1.3 0.98312 0.11198 johnny_darter heptageniidae
## 369 15 40 5 73 2 0.010 1.9 0.71828 0.63051 johnny_darter leptophlebiidae
## 370 15 43 5 93 5 0.013 2.4 1.00000 0.02519 johnny_darter leuctridae
## 371 15 44 5 61 2 0.008 1.6 0.81544 0.50933 johnny_darter nemouridae
## 372 15 45 5 51 2 0.007 1.3 0.88216 0.40046 johnny_darter perlidae
## 373 15 51 5 52 2 0.007 1.4 0.87614 0.41152 johnny_darter limnephilidae
## 374 15 54 5 74 4 0.010 1.9 0.99219 0.07362 johnny_darter philopotamidae
## 375 15 56 5 81 2 0.011 2.1 0.64560 0.70275 johnny_darter polycentropodidae
## 376 15 58 5 54 2 0.007 1.4 0.86366 0.43357 johnny_darter uenoidae
## 377 16 23 5 53 0 0.007 1.4 0.19489 1.00000 rainbow_darter sow_bugs
## 378 16 24 5 60 2 0.008 1.6 0.82275 0.49868 rainbow_darter scuds
## 379 16 25 5 65 2 0.009 1.7 0.78493 0.55118 rainbow_darter water_mites
## 380 16 26 5 97 3 0.013 2.5 0.80922 0.50987 rainbow_darter damselfly_nymphs
## 381 16 27 5 79 3 0.011 2.1 0.90678 0.33577 rainbow_darter alderfly_larvae
## 382 16 28 5 125 5 0.017 3.3 1.00000 0.11368 rainbow_darter dragonfly_nymphs
## 383 16 29 5 101 5 0.014 2.6 1.00000 0.03839 rainbow_darter riffle_beetles
## 384 16 30 5 131 4 0.018 3.4 0.85575 0.49067 rainbow_darter snails
## 385 16 31 5 55 2 0.007 1.4 0.85720 0.44454 rainbow_darter clams
## 386 16 32 5 68 2 0.009 1.8 0.76076 0.58167 rainbow_darter fishfly_larvae
## 387 16 33 5 47 2 0.006 1.2 0.90467 0.35608 rainbow_darter water_penny_beetles
## 388 16 34 5 70 3 0.009 1.8 0.93991 0.25592 rainbow_darter ameletidae
## 389 16 38 5 50 3 0.007 1.3 0.98312 0.11198 rainbow_darter heptageniidae
## 390 16 40 5 73 3 0.010 1.9 0.92994 0.28172 rainbow_darter leptophlebiidae
## 391 16 43 5 93 4 0.013 2.4 0.97481 0.16529 rainbow_darter leuctridae
## 392 16 44 5 61 2 0.008 1.6 0.81544 0.50933 rainbow_darter nemouridae
## 393 16 45 5 51 2 0.007 1.3 0.88216 0.40046 rainbow_darter perlidae
## 394 16 51 5 52 2 0.007 1.4 0.87614 0.41152 rainbow_darter limnephilidae
## 395 16 54 5 74 4 0.010 1.9 0.99219 0.07362 rainbow_darter philopotamidae
## 396 16 56 5 81 4 0.011 2.1 0.98758 0.10193 rainbow_darter polycentropodidae
## 397 16 58 5 54 2 0.007 1.4 0.86366 0.43357 rainbow_darter uenoidae
## 398 18 26 2 97 1 0.005 1.0 0.74607 0.75649 redbelly_dace damselfly_nymphs
## 399 18 28 2 125 2 0.007 1.3 1.00000 0.42267 redbelly_dace dragonfly_nymphs
## 400 18 29 2 101 2 0.005 1.1 1.00000 0.27541 redbelly_dace riffle_beetles
## 401 18 30 2 131 2 0.007 1.4 1.00000 0.46439 redbelly_dace snails
## 402 20 23 4 53 1 0.006 1.1 0.69445 0.72860 white_sucker sow_bugs
## 403 20 24 4 60 2 0.007 1.2 0.90879 0.37011 white_sucker scuds
## 404 20 25 4 65 2 0.007 1.4 0.88656 0.41674 white_sucker water_mites
## 405 20 26 4 97 2 0.011 2.0 0.68158 0.69729 white_sucker damselfly_nymphs
## 406 20 27 4 79 0 0.009 1.6 0.11734 1.00000 white_sucker alderfly_larvae
## 407 20 28 4 125 4 0.014 2.6 1.00000 0.17662 white_sucker dragonfly_nymphs
## 408 20 29 4 101 3 0.011 2.1 0.92559 0.35079 white_sucker riffle_beetles
## 409 20 30 4 131 4 0.014 2.7 1.00000 0.21353 white_sucker snails
## 410 20 31 4 55 2 0.006 1.1 0.92828 0.32384 white_sucker clams
## 411 20 32 4 68 0 0.007 1.4 0.17097 1.00000 white_sucker fishfly_larvae
## 412 20 34 4 70 4 0.008 1.5 1.00000 0.01671 white_sucker ameletidae
## 413 20 38 4 50 2 0.005 1.0 0.94508 0.27842 white_sucker heptageniidae
## 414 20 40 4 73 1 0.008 1.5 0.50901 0.85528 white_sucker leptophlebiidae
## 415 20 43 4 93 2 0.010 1.9 0.71266 0.66555 white_sucker leuctridae
## 416 20 44 4 61 2 0.007 1.3 0.90457 0.37942 white_sucker nemouridae
## 417 20 45 4 51 0 0.006 1.1 0.28754 1.00000 white_sucker perlidae
## 418 20 51 4 52 1 0.006 1.1 0.70354 0.72062 white_sucker limnephilidae
## 419 20 54 4 74 3 0.008 1.5 0.97903 0.16038 white_sucker philopotamidae
## 420 20 56 4 81 2 0.009 1.7 0.79708 0.56341 white_sucker polycentropodidae
## 421 20 58 4 54 1 0.006 1.1 0.68532 0.73641 white_sucker uenoidae
## 422 22 23 5 53 2 0.007 1.4 0.86998 0.42256 sessile_animals sow_bugs
## 423 22 24 5 60 2 0.008 1.6 0.82275 0.49868 sessile_animals scuds
## 424 22 25 5 65 0 0.009 1.7 0.12323 1.00000 sessile_animals water_mites
## 425 22 26 5 97 4 0.013 2.5 0.96876 0.19078 sessile_animals damselfly_nymphs
## 426 22 27 5 79 2 0.011 2.1 0.66423 0.68542 sessile_animals alderfly_larvae
## 427 22 28 5 125 4 0.017 3.3 0.88632 0.42840 sessile_animals dragonfly_nymphs
## 428 22 29 5 101 3 0.014 2.6 0.78152 0.54926 sessile_animals riffle_beetles
## 429 22 30 5 131 4 0.018 3.4 0.85575 0.49067 sessile_animals snails
## 430 22 31 5 55 3 0.007 1.4 0.97567 0.14280 sessile_animals clams
## 431 22 32 5 68 1 0.009 1.8 0.41833 0.89087 sessile_animals fishfly_larvae
## 432 22 33 5 47 1 0.006 1.2 0.64392 0.75853 sessile_animals water_penny_beetles
## 433 22 34 5 70 3 0.009 1.8 0.93991 0.25592 sessile_animals ameletidae
## 434 22 38 5 50 1 0.007 1.3 0.61062 0.78282 sessile_animals heptageniidae
## 435 22 40 5 73 2 0.010 1.9 0.71828 0.63051 sessile_animals leptophlebiidae
## 436 22 43 5 93 2 0.013 2.4 0.52959 0.79565 sessile_animals leuctridae
## 437 22 44 5 61 1 0.008 1.6 0.49067 0.85575 sessile_animals nemouridae
## 438 22 45 5 51 1 0.007 1.3 0.59954 0.79047 sessile_animals perlidae
## 439 22 51 5 52 0 0.007 1.4 0.20210 1.00000 sessile_animals limnephilidae
## 440 22 54 5 74 3 0.010 1.9 0.92638 0.29051 sessile_animals philopotamidae
## 441 22 56 5 81 2 0.011 2.1 0.64560 0.70275 sessile_animals polycentropodidae
## 442 22 58 5 54 1 0.007 1.4 0.56643 0.81212 sessile_animals uenoidae
## 443 23 24 53 60 33 0.086 16.6 1.00000 0.00000 sow_bugs scuds
## 444 23 25 53 65 30 0.093 17.9 0.99999 0.00005 sow_bugs water_mites
## 445 23 26 53 97 33 0.139 26.8 0.98534 0.03196 sow_bugs damselfly_nymphs
## 446 23 27 53 79 17 0.114 21.8 0.07798 0.96025 sow_bugs alderfly_larvae
## 447 23 28 53 125 35 0.180 34.5 0.62920 0.50379 sow_bugs dragonfly_nymphs
## 448 23 29 53 101 30 0.145 27.9 0.80134 0.30069 sow_bugs riffle_beetles
## 449 23 30 53 131 43 0.188 36.2 0.99561 0.01228 sow_bugs snails
## 450 23 31 53 55 22 0.079 15.2 0.99487 0.01314 sow_bugs clams
## 451 23 32 53 68 8 0.098 18.8 0.00015 0.99997 sow_bugs fishfly_larvae
## 452 23 33 53 47 10 0.068 13.0 0.17705 0.90603 sow_bugs water_penny_beetles
## 453 23 34 53 70 22 0.101 19.3 0.85655 0.23175 sow_bugs ameletidae
## 454 23 35 53 13 3 0.019 3.6 0.49471 0.74959 sow_bugs caenidae
## 455 23 36 53 5 3 0.007 1.4 0.97888 0.13002 sow_bugs ephemerellidae
## 456 23 38 53 50 11 0.072 13.8 0.19972 0.88919 sow_bugs heptageniidae
## 457 23 39 53 12 2 0.017 3.3 0.30679 0.89219 sow_bugs leptohyphidae
## 458 23 40 53 73 9 0.105 20.2 0.00012 0.99997 sow_bugs leptophlebiidae
## 459 23 42 53 22 5 0.032 6.1 0.39682 0.78370 sow_bugs chloroperlidae
## 460 23 43 53 93 20 0.134 25.7 0.04700 0.97732 sow_bugs leuctridae
## 461 23 44 53 61 6 0.088 16.8 0.00008 0.99999 sow_bugs nemouridae
## 462 23 45 53 51 7 0.073 14.1 0.00633 0.99815 sow_bugs perlidae
## 463 23 46 53 22 1 0.032 6.1 0.00539 0.99950 sow_bugs perlodidae
## 464 23 49 53 22 11 0.032 6.1 0.99576 0.01518 sow_bugs hydroptilidae
## 465 23 50 53 35 3 0.050 9.7 0.00290 0.99949 sow_bugs lepidostomatidae
## 466 23 51 53 52 7 0.075 14.4 0.00487 0.99862 sow_bugs limnephilidae
## 467 23 52 53 4 0 0.006 1.1 0.27140 1.00000 sow_bugs molannidae
## 468 23 53 53 6 0 0.009 1.7 0.13965 1.00000 sow_bugs odontoceridae
## 469 23 54 53 74 21 0.106 20.4 0.64080 0.48807 sow_bugs philopotamidae
## 470 23 55 53 9 2 0.013 2.5 0.52649 0.76457 sow_bugs phryganeidae
## 471 23 56 53 81 23 0.116 22.4 0.64650 0.48015 sow_bugs polycentropodidae
## 472 23 57 53 27 2 0.039 7.5 0.00660 0.99898 sow_bugs rhyacophilidae
## 473 23 58 53 54 9 0.078 14.9 0.02359 0.99115 sow_bugs uenoidae
## 474 24 25 60 65 31 0.106 20.3 0.99986 0.00046 scuds water_mites
## 475 24 26 60 97 36 0.158 30.3 0.97327 0.05287 scuds damselfly_nymphs
## 476 24 27 60 79 15 0.129 24.7 0.00157 0.99949 scuds alderfly_larvae
## 477 24 28 60 125 40 0.203 39.1 0.67880 0.44565 scuds dragonfly_nymphs
## 478 24 29 60 101 32 0.164 31.6 0.61455 0.50818 scuds riffle_beetles
## 479 24 30 60 131 46 0.213 40.9 0.97028 0.06196 scuds snails
## 480 24 31 60 55 25 0.090 17.2 0.99760 0.00649 scuds clams
## 481 24 32 60 68 13 0.111 21.2 0.00508 0.99822 scuds fishfly_larvae
## 482 24 33 60 47 10 0.076 14.7 0.06235 0.97242 scuds water_penny_beetles
## 483 24 34 60 70 19 0.114 21.9 0.22193 0.86280 scuds ameletidae
## 484 24 35 60 13 5 0.021 4.1 0.81538 0.38151 scuds caenidae
## 485 24 36 60 5 1 0.008 1.6 0.50132 0.85007 scuds ephemerellidae
## 486 24 38 60 50 7 0.081 15.6 0.00133 0.99967 scuds heptageniidae
## 487 24 39 60 12 0 0.020 3.8 0.00946 1.00000 scuds leptohyphidae
## 488 24 40 60 73 11 0.119 22.8 0.00009 0.99998 scuds leptophlebiidae
## 489 24 42 60 22 5 0.036 6.9 0.25555 0.87934 scuds chloroperlidae
## 490 24 43 60 93 22 0.151 29.1 0.02012 0.99103 scuds leuctridae
## 491 24 44 60 61 12 0.099 19.1 0.01271 0.99519 scuds nemouridae
## 492 24 45 60 51 5 0.083 15.9 0.00005 0.99999 scuds perlidae
## 493 24 46 60 22 5 0.036 6.9 0.25555 0.87934 scuds perlodidae
## 494 24 49 60 22 9 0.036 6.9 0.89834 0.21109 scuds hydroptilidae
## 495 24 50 60 35 11 0.057 10.9 0.59554 0.56366 scuds lepidostomatidae
## 496 24 51 60 52 14 0.085 16.2 0.27218 0.83214 scuds limnephilidae
## 497 24 52 60 4 1 0.007 1.2 0.62989 0.77979 scuds molannidae
## 498 24 53 60 6 0 0.010 1.9 0.10182 1.00000 scuds odontoceridae
## 499 24 54 60 74 26 0.120 23.1 0.85974 0.22318 scuds philopotamidae
## 500 24 55 60 9 2 0.015 2.8 0.42642 0.83181 scuds phryganeidae
## 501 24 56 60 81 26 0.132 25.3 0.64684 0.47522 scuds polycentropodidae
## 502 24 57 60 27 8 0.044 8.4 0.51931 0.65607 scuds rhyacophilidae
## 503 24 58 60 54 12 0.088 16.9 0.06296 0.97077 scuds uenoidae
## 504 25 26 65 97 37 0.171 32.8 0.92263 0.13199 water_mites damselfly_nymphs
## 505 25 27 65 79 28 0.139 26.7 0.70740 0.40644 water_mites alderfly_larvae
## 506 25 28 65 125 48 0.220 42.3 0.97719 0.04746 water_mites dragonfly_nymphs
## 507 25 29 65 101 47 0.178 34.2 0.99998 0.00007 water_mites riffle_beetles
## 508 25 30 65 131 50 0.231 44.3 0.97941 0.04431 water_mites snails
## 509 25 31 65 55 20 0.097 18.6 0.73845 0.38065 water_mites clams
## 510 25 32 65 68 21 0.120 23.0 0.31534 0.78866 water_mites fishfly_larvae
## 511 25 33 65 47 19 0.083 15.9 0.89761 0.17888 water_mites water_penny_beetles
## 512 25 34 65 70 29 0.123 23.7 0.96644 0.06462 water_mites ameletidae
## 513 25 35 65 13 5 0.023 4.4 0.75214 0.46460 water_mites caenidae
## 514 25 36 65 5 2 0.009 1.7 0.78493 0.55118 water_mites ephemerellidae
## 515 25 37 65 3 1 0.005 1.0 0.73491 0.71292 water_mites ephemeridae
## 516 25 38 65 50 18 0.088 16.9 0.70970 0.41778 water_mites heptageniidae
## 517 25 39 65 12 5 0.021 4.1 0.81883 0.38124 water_mites leptohyphidae
## 518 25 40 65 73 24 0.129 24.7 0.47490 0.64709 water_mites leptophlebiidae
## 519 25 42 65 22 8 0.039 7.4 0.69765 0.48199 water_mites chloroperlidae
## 520 25 43 65 93 37 0.164 31.5 0.96692 0.06286 water_mites leuctridae
## 521 25 44 65 61 20 0.108 20.7 0.48287 0.64473 water_mites nemouridae
## 522 25 45 65 51 17 0.090 17.3 0.53569 0.60097 water_mites perlidae
## 523 25 46 65 22 5 0.039 7.4 0.17634 0.92488 water_mites perlodidae
## 524 25 47 65 3 2 0.005 1.0 0.96239 0.26509 water_mites glossosomatidae
## 525 25 49 65 22 12 0.039 7.4 0.99092 0.02844 water_mites hydroptilidae
## 526 25 50 65 35 12 0.062 11.8 0.60623 0.54937 water_mites lepidostomatidae
## 527 25 51 65 52 20 0.092 17.6 0.83989 0.25618 water_mites limnephilidae
## 528 25 52 65 4 1 0.007 1.4 0.58326 0.81165 water_mites molannidae
## 529 25 53 65 6 3 0.011 2.0 0.89816 0.32831 water_mites odontoceridae
## 530 25 54 65 74 33 0.130 25.1 0.99580 0.01006 water_mites philopotamidae
## 531 25 55 65 9 1 0.016 3.0 0.12980 0.97806 water_mites phryganeidae
## 532 25 56 65 81 29 0.143 27.4 0.73990 0.36879 water_mites polycentropodidae
## 533 25 57 65 27 12 0.048 9.1 0.92774 0.15045 water_mites rhyacophilidae
## 534 25 58 65 54 17 0.095 18.3 0.39852 0.72528 water_mites uenoidae
## 535 26 27 97 79 44 0.208 39.9 0.91093 0.14625 damselfly_nymphs alderfly_larvae
## 536 26 28 97 125 78 0.329 63.2 1.00000 0.00001 damselfly_nymphs dragonfly_nymphs
## 537 26 29 97 101 69 0.266 51.0 1.00000 0.00000 damselfly_nymphs riffle_beetles
## 538 26 30 97 131 77 0.345 66.2 0.99980 0.00064 damselfly_nymphs snails
## 539 26 31 97 55 37 0.145 27.8 0.99912 0.00256 damselfly_nymphs clams
## 540 26 32 97 68 35 0.179 34.4 0.63514 0.48252 damselfly_nymphs fishfly_larvae
## 541 26 33 97 47 28 0.124 23.7 0.94514 0.10355 damselfly_nymphs water_penny_beetles
## 542 26 34 97 70 44 0.184 35.4 0.99703 0.00719 damselfly_nymphs ameletidae
## 543 26 35 97 13 12 0.034 6.6 0.99991 0.00142 damselfly_nymphs caenidae
## 544 26 36 97 5 3 0.013 2.5 0.80922 0.50987 damselfly_nymphs ephemerellidae
## 545 26 37 97 3 2 0.008 1.5 0.87304 0.50785 damselfly_nymphs ephemeridae
## 546 26 38 97 50 30 0.132 25.3 0.95793 0.08137 damselfly_nymphs heptageniidae
## 547 26 39 97 12 8 0.032 6.1 0.92840 0.19631 damselfly_nymphs leptohyphidae
## 548 26 40 97 73 30 0.192 36.9 0.02875 0.98605 damselfly_nymphs leptophlebiidae
## 549 26 42 97 22 11 0.058 11.1 0.56888 0.60959 damselfly_nymphs chloroperlidae
## 550 26 43 97 93 45 0.245 47.0 0.33411 0.76344 damselfly_nymphs leuctridae
## 551 26 44 97 61 27 0.161 30.8 0.15184 0.90974 damselfly_nymphs nemouridae
## 552 26 45 97 51 31 0.134 25.8 0.96989 0.06062 damselfly_nymphs perlidae
## 553 26 46 97 22 12 0.058 11.1 0.73440 0.43112 damselfly_nymphs perlodidae
## 554 26 47 97 3 1 0.008 1.5 0.49215 0.88081 damselfly_nymphs glossosomatidae
## 555 26 49 97 22 13 0.058 11.1 0.86025 0.26560 damselfly_nymphs hydroptilidae
## 556 26 50 97 35 11 0.092 17.7 0.01000 0.99664 damselfly_nymphs lepidostomatidae
## 557 26 51 97 52 25 0.137 26.3 0.40114 0.71736 damselfly_nymphs limnephilidae
## 558 26 52 97 4 1 0.011 2.0 0.30271 0.94198 damselfly_nymphs molannidae
## 559 26 53 97 6 1 0.016 3.0 0.10092 0.98648 damselfly_nymphs odontoceridae
## 560 26 54 97 74 42 0.195 37.4 0.93550 0.11111 damselfly_nymphs philopotamidae
## 561 26 55 97 9 6 0.024 4.5 0.91023 0.25910 damselfly_nymphs phryganeidae
## 562 26 56 97 81 46 0.213 40.9 0.94864 0.09034 damselfly_nymphs polycentropodidae
## 563 26 57 97 27 10 0.071 13.6 0.09584 0.95782 damselfly_nymphs rhyacophilidae
## 564 26 58 97 54 30 0.142 27.3 0.84929 0.23827 damselfly_nymphs uenoidae
## 565 27 28 79 125 64 0.268 51.4 0.99998 0.00008 alderfly_larvae dragonfly_nymphs
## 566 27 29 79 101 50 0.216 41.6 0.99582 0.00964 alderfly_larvae riffle_beetles
## 567 27 30 79 131 54 0.281 53.9 0.57347 0.55156 alderfly_larvae snails
## 568 27 31 79 55 24 0.118 22.6 0.72867 0.38755 alderfly_larvae clams
## 569 27 32 79 68 49 0.146 28.0 1.00000 0.00000 alderfly_larvae fishfly_larvae
## 570 27 33 79 47 33 0.101 19.3 1.00000 0.00000 alderfly_larvae water_penny_beetles
## 571 27 34 79 70 37 0.150 28.8 0.99592 0.00962 alderfly_larvae ameletidae
## 572 27 35 79 13 7 0.028 5.3 0.89452 0.24886 alderfly_larvae caenidae
## 573 27 36 79 5 2 0.011 2.1 0.66423 0.68542 alderfly_larvae ephemerellidae
## 574 27 37 79 3 3 0.006 1.2 1.00000 0.06810 alderfly_larvae ephemeridae
## 575 27 38 79 50 31 0.107 20.6 0.99986 0.00048 alderfly_larvae heptageniidae
## 576 27 39 79 12 11 0.026 4.9 0.99999 0.00029 alderfly_larvae leptohyphidae
## 577 27 40 79 73 47 0.156 30.0 1.00000 0.00000 alderfly_larvae leptophlebiidae
## 578 27 42 79 22 15 0.047 9.1 0.99844 0.00634 alderfly_larvae chloroperlidae
## 579 27 43 79 93 59 0.199 38.3 1.00000 0.00000 alderfly_larvae leuctridae
## 580 27 44 79 61 38 0.131 25.1 0.99999 0.00005 alderfly_larvae nemouridae
## 581 27 45 79 51 42 0.109 21.0 1.00000 0.00000 alderfly_larvae perlidae
## 582 27 46 79 22 12 0.047 9.1 0.94282 0.13024 alderfly_larvae perlodidae
## 583 27 47 79 3 2 0.006 1.2 0.93190 0.36790 alderfly_larvae glossosomatidae
## 584 27 49 79 22 9 0.047 9.1 0.58544 0.59613 alderfly_larvae hydroptilidae
## 585 27 50 79 35 19 0.075 14.4 0.97295 0.06050 alderfly_larvae lepidostomatidae
## 586 27 51 79 52 30 0.111 21.4 0.99861 0.00388 alderfly_larvae limnephilidae
## 587 27 52 79 4 2 0.009 1.6 0.80976 0.54556 alderfly_larvae molannidae
## 588 27 53 79 6 2 0.013 2.5 0.52026 0.78826 alderfly_larvae odontoceridae
## 589 27 54 79 74 43 0.159 30.4 0.99996 0.00014 alderfly_larvae philopotamidae
## 590 27 55 79 9 7 0.019 3.7 0.99610 0.02661 alderfly_larvae phryganeidae
## 591 27 56 79 81 41 0.174 33.3 0.99237 0.01663 alderfly_larvae polycentropodidae
## 592 27 57 79 27 17 0.058 11.1 0.99631 0.01195 alderfly_larvae rhyacophilidae
## 593 27 58 79 54 39 0.116 22.2 1.00000 0.00000 alderfly_larvae uenoidae
## 594 28 29 125 101 80 0.342 65.8 1.00000 0.00001 dragonfly_nymphs riffle_beetles
## 595 28 30 125 131 90 0.444 85.3 0.95421 0.08594 dragonfly_nymphs snails
## 596 28 31 125 55 43 0.186 35.8 0.99582 0.01123 dragonfly_nymphs clams
## 597 28 32 125 68 52 0.231 44.3 0.99594 0.01024 dragonfly_nymphs fishfly_larvae
## 598 28 33 125 47 43 0.159 30.6 1.00000 0.00000 dragonfly_nymphs water_penny_beetles
## 599 28 34 125 70 56 0.237 45.6 0.99978 0.00072 dragonfly_nymphs ameletidae
## 600 28 35 125 13 12 0.044 8.5 0.99700 0.02610 dragonfly_nymphs caenidae
## 601 28 36 125 5 5 0.017 3.3 1.00000 0.11368 dragonfly_nymphs ephemerellidae
## 602 28 37 125 3 3 0.010 2.0 1.00000 0.27362 dragonfly_nymphs ephemeridae
## 603 28 38 125 50 44 0.170 32.6 0.99999 0.00003 dragonfly_nymphs heptageniidae
## 604 28 39 125 12 10 0.041 7.8 0.96155 0.14476 dragonfly_nymphs leptohyphidae
## 605 28 40 125 73 58 0.248 47.5 0.99976 0.00077 dragonfly_nymphs leptophlebiidae
## 606 28 42 125 22 19 0.075 14.3 0.99577 0.01909 dragonfly_nymphs chloroperlidae
## 607 28 43 125 93 76 0.315 60.5 1.00000 0.00000 dragonfly_nymphs leuctridae
## 608 28 44 125 61 51 0.207 39.7 0.99997 0.00014 dragonfly_nymphs nemouridae
## 609 28 45 125 51 46 0.173 33.2 1.00000 0.00000 dragonfly_nymphs perlidae
## 610 28 46 125 22 19 0.075 14.3 0.99577 0.01909 dragonfly_nymphs perlodidae
## 611 28 47 125 3 3 0.010 2.0 1.00000 0.27362 dragonfly_nymphs glossosomatidae
## 612 28 49 125 22 15 0.075 14.3 0.70710 0.47405 dragonfly_nymphs hydroptilidae
## 613 28 50 125 35 32 0.119 22.8 0.99999 0.00012 dragonfly_nymphs lepidostomatidae
## 614 28 51 125 52 44 0.176 33.9 0.99993 0.00032 dragonfly_nymphs limnephilidae
## 615 28 52 125 4 3 0.014 2.6 0.82338 0.56461 dragonfly_nymphs molannidae
## 616 28 53 125 6 6 0.020 3.9 1.00000 0.07295 dragonfly_nymphs odontoceridae
## 617 28 54 125 74 62 0.251 48.2 1.00000 0.00001 dragonfly_nymphs philopotamidae
## 618 28 55 125 9 8 0.031 5.9 0.98107 0.11649 dragonfly_nymphs phryganeidae
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## 625 29 33 101 47 34 0.129 24.7 0.99958 0.00140 riffle_beetles water_penny_beetles
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## 628 29 36 101 5 3 0.014 2.6 0.78152 0.54926 riffle_beetles ephemerellidae
## 629 29 37 101 3 3 0.008 1.6 1.00000 0.14351 riffle_beetles ephemeridae
## 630 29 38 101 50 37 0.137 26.3 0.99991 0.00033 riffle_beetles heptageniidae
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## 632 29 40 101 73 42 0.200 38.4 0.88896 0.17814 riffle_beetles leptophlebiidae
## 633 29 42 101 22 16 0.060 11.6 0.98864 0.03585 riffle_beetles chloroperlidae
## 634 29 43 101 93 59 0.255 48.9 0.99893 0.00273 riffle_beetles leuctridae
## 635 29 44 101 61 37 0.167 32.1 0.95390 0.08516 riffle_beetles nemouridae
## 636 29 45 101 51 37 0.140 26.8 0.99981 0.00067 riffle_beetles perlidae
## 637 29 46 101 22 13 0.060 11.6 0.80863 0.33835 riffle_beetles perlodidae
## 638 29 47 101 3 2 0.008 1.6 0.85649 0.53923 riffle_beetles glossosomatidae
## 639 29 49 101 22 15 0.060 11.6 0.96415 0.09119 riffle_beetles hydroptilidae
## 640 29 50 101 35 13 0.096 18.4 0.03283 0.98675 riffle_beetles lepidostomatidae
## 641 29 51 101 52 33 0.142 27.4 0.97770 0.04660 riffle_beetles limnephilidae
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## 643 29 53 101 6 2 0.016 3.2 0.29332 0.91659 riffle_beetles odontoceridae
## 644 29 54 101 74 53 0.203 38.9 0.99999 0.00002 riffle_beetles philopotamidae
## 645 29 55 101 9 6 0.025 4.7 0.88745 0.30315 riffle_beetles phryganeidae
## 646 29 56 101 81 51 0.222 42.6 0.99549 0.01028 riffle_beetles polycentropodidae
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## 650 30 32 131 68 40 0.242 46.4 0.02871 0.98678 snails fishfly_larvae
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## 658 30 40 131 73 35 0.259 49.8 0.00000 1.00000 snails leptophlebiidae
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## 661 30 44 131 61 30 0.217 41.6 0.00013 0.99997 snails nemouridae
## 662 30 45 131 51 32 0.181 34.8 0.20914 0.87583 snails perlidae
## 663 30 46 131 22 13 0.078 15.0 0.22817 0.88756 snails perlodidae
## 664 30 47 131 3 2 0.011 2.0 0.68471 0.76257 snails glossosomatidae
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## 669 30 53 131 6 0 0.021 4.1 0.00086 1.00000 snails odontoceridae
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## 672 30 56 131 81 51 0.288 55.3 0.11879 0.93234 snails polycentropodidae
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## 676 31 33 55 47 18 0.070 13.5 0.96741 0.06874 clams water_penny_beetles
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## 685 31 44 55 61 13 0.091 17.5 0.08506 0.95794 clams nemouridae
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## 687 31 46 55 22 5 0.033 6.3 0.35323 0.81518 clams perlodidae
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## 869 44 53 61 6 6 0.010 1.9 1.00000 0.00086 nemouridae odontoceridae
## 870 44 54 61 74 38 0.122 23.5 1.00000 0.00000 nemouridae philopotamidae
## 871 44 55 61 9 3 0.015 2.9 0.69118 0.58727 nemouridae phryganeidae
## 872 44 56 61 81 30 0.134 25.7 0.93234 0.11879 nemouridae polycentropodidae
## 873 44 57 61 27 24 0.045 8.6 1.00000 0.00000 nemouridae rhyacophilidae
## 874 44 58 61 54 36 0.089 17.2 1.00000 0.00000 nemouridae uenoidae
## 875 45 46 51 22 14 0.030 5.8 0.99998 0.00011 perlidae perlodidae
## 876 45 49 51 22 5 0.030 5.8 0.44248 0.74878 perlidae hydroptilidae
## 877 45 50 51 35 18 0.048 9.3 0.99990 0.00044 perlidae lepidostomatidae
## 878 45 51 51 52 28 0.072 13.8 1.00000 0.00000 perlidae limnephilidae
## 879 45 52 51 4 2 0.006 1.1 0.94193 0.28741 perlidae molannidae
## 880 45 53 51 6 3 0.008 1.6 0.95621 0.19190 perlidae odontoceridae
## 881 45 54 51 74 31 0.102 19.7 0.99996 0.00015 perlidae philopotamidae
## 882 45 55 51 9 5 0.012 2.4 0.98827 0.05761 perlidae phryganeidae
## 883 45 56 51 81 23 0.112 21.5 0.74490 0.37106 perlidae polycentropodidae
## 884 45 57 51 27 13 0.037 7.2 0.99777 0.00795 perlidae rhyacophilidae
## 885 45 58 51 54 31 0.075 14.3 1.00000 0.00000 perlidae uenoidae
## 886 46 49 22 22 3 0.013 2.5 0.76843 0.47629 perlodidae hydroptilidae
## 887 46 50 22 35 10 0.021 4.0 0.99972 0.00156 perlodidae lepidostomatidae
## 888 46 51 22 52 11 0.031 6.0 0.99650 0.01291 perlodidae limnephilidae
## 889 46 54 22 74 10 0.044 8.5 0.82697 0.31388 perlodidae philopotamidae
## 890 46 55 22 9 2 0.005 1.0 0.93027 0.27517 perlodidae phryganeidae
## 891 46 56 22 81 8 0.048 9.3 0.36350 0.79188 perlodidae polycentropodidae
## 892 46 57 22 27 7 0.016 3.1 0.99548 0.01933 perlodidae rhyacophilidae
## 893 46 58 22 54 15 0.032 6.2 1.00000 0.00004 perlodidae uenoidae
## 894 47 54 3 74 2 0.006 1.2 0.94418 0.33028 glossosomatidae philopotamidae
## 895 47 56 3 81 2 0.007 1.3 0.92653 0.38316 glossosomatidae polycentropodidae
## 896 49 50 22 35 4 0.021 4.0 0.62974 0.59950 hydroptilidae lepidostomatidae
## 897 49 51 22 52 3 0.031 6.0 0.10087 0.96856 hydroptilidae limnephilidae
## 898 49 54 22 74 12 0.044 8.5 0.96801 0.08113 hydroptilidae philopotamidae
## 899 49 55 22 9 1 0.005 1.0 0.72483 0.67383 hydroptilidae phryganeidae
## 900 49 56 22 81 17 0.048 9.3 0.99993 0.00045 hydroptilidae polycentropodidae
## 901 49 57 22 27 4 0.016 3.1 0.82372 0.37408 hydroptilidae rhyacophilidae
## 902 49 58 22 54 7 0.032 6.2 0.75069 0.42655 hydroptilidae uenoidae
## 903 50 51 35 52 26 0.049 9.5 1.00000 0.00000 lepidostomatidae limnephilidae
## 904 50 53 35 6 6 0.006 1.1 1.00000 0.00003 lepidostomatidae odontoceridae
## 905 50 54 35 74 20 0.070 13.5 0.99612 0.01115 lepidostomatidae philopotamidae
## 906 50 55 35 9 1 0.009 1.6 0.48747 0.84346 lepidostomatidae phryganeidae
## 907 50 56 35 81 16 0.077 14.8 0.74520 0.38844 lepidostomatidae polycentropodidae
## 908 50 57 35 27 19 0.026 4.9 1.00000 0.00000 lepidostomatidae rhyacophilidae
## 909 50 58 35 54 20 0.051 9.8 0.99999 0.00006 lepidostomatidae uenoidae
## 910 51 52 52 4 4 0.006 1.1 1.00000 0.00493 limnephilidae molannidae
## 911 51 53 52 6 6 0.008 1.6 1.00000 0.00032 limnephilidae odontoceridae
## 912 51 54 52 74 27 0.104 20.0 0.99326 0.01612 limnephilidae philopotamidae
## 913 51 55 52 9 3 0.013 2.4 0.79790 0.45877 limnephilidae phryganeidae
## 914 51 56 52 81 16 0.114 21.9 0.03587 0.98376 limnephilidae polycentropodidae
## 915 51 57 52 27 18 0.038 7.3 1.00000 0.00000 limnephilidae rhyacophilidae
## 916 51 58 52 54 26 0.076 14.6 0.99999 0.00006 limnephilidae uenoidae
## 917 52 54 4 74 3 0.008 1.5 0.97903 0.16038 molannidae philopotamidae
## 918 52 56 4 81 2 0.009 1.7 0.79708 0.56341 molannidae polycentropodidae
## 919 52 58 4 54 3 0.006 1.1 0.99424 0.06815 molannidae uenoidae
## 920 53 54 6 74 6 0.012 2.3 1.00000 0.00288 odontoceridae philopotamidae
## 921 53 56 6 81 3 0.013 2.5 0.79276 0.50156 odontoceridae polycentropodidae
## 922 53 58 6 54 3 0.009 1.7 0.94632 0.21899 odontoceridae uenoidae
## 923 54 55 74 9 5 0.018 3.5 0.92107 0.23196 philopotamidae phryganeidae
## 924 54 56 74 81 44 0.163 31.2 0.99997 0.00011 philopotamidae polycentropodidae
## 925 54 57 74 27 20 0.054 10.4 0.99999 0.00006 philopotamidae rhyacophilidae
## 926 54 58 74 54 30 0.108 20.8 0.99924 0.00223 philopotamidae uenoidae
## 927 55 56 9 81 4 0.020 3.8 0.68989 0.57408 phryganeidae polycentropodidae
## 928 55 57 9 27 1 0.007 1.3 0.63101 0.75233 phryganeidae rhyacophilidae
## 929 55 58 9 54 7 0.013 2.5 0.99982 0.00226 phryganeidae uenoidae
## 930 56 57 81 27 18 0.059 11.4 0.99856 0.00528 polycentropodidae rhyacophilidae
## 931 56 58 81 54 30 0.119 22.8 0.99380 0.01474 polycentropodidae uenoidae
## 932 57 58 27 54 20 0.040 7.6 1.00000 0.00000 rhyacophilidae uenoidae# network stuff
library(asnipe)
library(igraph)
assoc=streambioticsum_suff_biotic_bin_t_3 #transpose the data into group-by-individual
mat=get_network(t(assoc), association_index="SRI") #create adjacency matrix with "simple ratio index"## Generating 58 x 58 matrixg.streams=graph_from_adjacency_matrix(mat, "undirected", weighted=T) #make into igraph object
plot(g.streams, edge.width=E(g.streams)$weight*5, vertex.label="")
com=cluster_louvain(g.streams)
com## IGRAPH clustering multi level, groups: 4, mod: 0.1
## + groups:
## $`1`
## [1] "streambioticsum_suff_mtdusky" "streambioticsum_suff_nodusky" "streambioticsum_suff_twolined" "common_shiner" "creek_chub" "sow_bugs" "scuds" "water_mites" "damselfly_nymphs" "alderfly_larvae" "dragonfly_nymphs" "riffle_beetles" "snails" "clams" "ameletidae" "leuctridae" "hydroptilidae" "philopotamidae"
## [19] "polycentropodidae"
##
## $`2`
## [1] "streambioticsum_suff_longtail" "streambioticsum_suff_red" "streambioticsum_suff_mole" "streambioticsum_suff_redback" "streambioticsum_suff_slimy" "blacknose_dace" "redbelly_dace" "redside_dace" "fishfly_larvae" "water_penny_beetles" "caenidae" "ephemerellidae" "heptageniidae" "leptohyphidae" "leptophlebiidae" "capniidae" "chloroperlidae" "nemouridae"
## [19] "perlidae" "perlodidae" "glossosomatidae" "goeridae" "lepidostomatidae" "limnephilidae" "molannidae" "odontoceridae" "rhyacophilidae" "uenoidae"
##
## $`3`
## [1] "bluegill_sunfish" "central_stoneroller_minnow" "green_sunfish" "yellow_bullhead_catfish"
## + ... omitted several groups/verticescom$memberships## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58]
## [1,] 1 1 1 2 2 2 2 2 3 4 4 1 1 4 5 5 5 3 3 5 4 5 1 1 1 1 1 1 1 1 1 2 3 1 3 3 5 3 3 3 3 3 1 3 3 3 2 3 1 2 2 2 2 1 5 1 2 3
## [2,] 1 1 1 2 2 2 2 2 2 3 3 1 1 3 4 4 4 2 2 4 3 4 1 1 1 1 1 1 1 1 1 2 2 1 2 2 4 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 1 4 1 2 2set.seed(2)
plot(com, g.streams, edge.width=E(g.streams)$weight*0.5)
library(RColorBrewer)
colors=brewer.pal(length(com),'Accent') #make a color palette
V(g.streams)$color=colors[membership(com)] #assign each vertex a color based on the community assignment
set.seed(2)
plot(com,g.streams, edge.width=E(g.streams)$weight*0.1,edge.color="lightgrey",vertex.label.cex=0.8,vertex.size=7,rescale=TRUE)
# number of species
S <- vcount(g.streams)
# number of interactions
L <- ecount(g.streams)
# average number of interactions species
L.S <- L/S
# network connectance
C <- L/S^2
# Calculate connectance
connectance <- ecount(g.streams) / vcount(g.streams)^2
# Print connectance va
print(paste0('Connectance of stream network =', round(connectance,2)))## [1] "Connectance of stream network =0.34"links.per.species <- ecount(g.streams) / vcount(g.streams)
## Mean generality of species in the network
Generality <- function(M){
return(sum(colSums(M))/sum((colSums(M)!=0)));
}
Generality(mat)## [1] 5.497505 ## Mean vulnerability of the species in the network
Vulnerability <- function(M){
return(sum(rowSums(M))/sum((rowSums(M)!=0)));
}
Vulnerability(mat)## [1] 5.497505 ## In-degree or number of prey of all species in the network
InDegree <- function(M){
return(colSums(M));
}
InDegree(mat)## streambioticsum_suff_mtdusky streambioticsum_suff_nodusky streambioticsum_suff_twolined streambioticsum_suff_longtail streambioticsum_suff_red streambioticsum_suff_mole streambioticsum_suff_redback streambioticsum_suff_slimy blacknose_dace bluegill_sunfish central_stoneroller_minnow common_shiner creek_chub green_sunfish johnny_darter rainbow_darter rainbow_trout redbelly_dace redside_dace white_sucker
## 4.89714583 8.89001874 11.70048301 1.89869056 5.30426291 0.61522089 9.31578346 3.32639466 5.54438309 1.03429748 1.34680364 0.15619359 7.35882458 1.43295707 1.48310026 1.82011997 0.34574673 0.81506562 0.60528726 1.33444515
## yellow_bullhead_catfish sessile_animals sow_bugs scuds water_mites damselfly_nymphs alderfly_larvae dragonfly_nymphs riffle_beetles snails clams fishfly_larvae water_penny_beetles ameletidae caenidae ephemerellidae ephemeridae heptageniidae leptohyphidae leptophlebiidae
## 0.45572537 1.20451477 5.43878047 6.00373795 7.76299495 9.13365758 10.56696534 11.59315741 10.66328242 9.12881510 7.04017091 10.66986556 9.40509233 9.48138099 3.19268020 1.72422304 1.34038345 9.21363392 4.27171342 10.95290191
## capniidae chloroperlidae leuctridae nemouridae perlidae perlodidae glossosomatidae goeridae hydroptilidae lepidostomatidae limnephilidae molannidae odontoceridae philopotamidae phryganeidae polycentropodidae rhyacophilidae uenoidae
## 0.05620723 6.33085611 12.15424733 10.75091308 10.08058338 5.43053801 1.28113619 0.34411082 3.84671795 7.58551467 8.76067854 1.68155509 2.21599302 10.64278047 2.57876862 9.16869175 7.16836525 10.30872674 ## Out-degree or number of predators of all species in the network
OutDegree <- function(M){
return(rowSums(M));
}
OutDegree(mat)## streambioticsum_suff_mtdusky streambioticsum_suff_nodusky streambioticsum_suff_twolined streambioticsum_suff_longtail streambioticsum_suff_red streambioticsum_suff_mole streambioticsum_suff_redback streambioticsum_suff_slimy blacknose_dace bluegill_sunfish central_stoneroller_minnow common_shiner creek_chub green_sunfish johnny_darter rainbow_darter rainbow_trout redbelly_dace redside_dace white_sucker
## 4.89714583 8.89001874 11.70048301 1.89869056 5.30426291 0.61522089 9.31578346 3.32639466 5.54438309 1.03429748 1.34680364 0.15619359 7.35882458 1.43295707 1.48310026 1.82011997 0.34574673 0.81506562 0.60528726 1.33444515
## yellow_bullhead_catfish sessile_animals sow_bugs scuds water_mites damselfly_nymphs alderfly_larvae dragonfly_nymphs riffle_beetles snails clams fishfly_larvae water_penny_beetles ameletidae caenidae ephemerellidae ephemeridae heptageniidae leptohyphidae leptophlebiidae
## 0.45572537 1.20451477 5.43878047 6.00373795 7.76299495 9.13365758 10.56696534 11.59315741 10.66328242 9.12881510 7.04017091 10.66986556 9.40509233 9.48138099 3.19268020 1.72422304 1.34038345 9.21363392 4.27171342 10.95290191
## capniidae chloroperlidae leuctridae nemouridae perlidae perlodidae glossosomatidae goeridae hydroptilidae lepidostomatidae limnephilidae molannidae odontoceridae philopotamidae phryganeidae polycentropodidae rhyacophilidae uenoidae
## 0.05620723 6.33085611 12.15424733 10.75091308 10.08058338 5.43053801 1.28113619 0.34411082 3.84671795 7.58551467 8.76067854 1.68155509 2.21599302 10.64278047 2.57876862 9.16869175 7.16836525 10.30872674#occupancy - all taxa
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_season.det.formula <- ~ scale(seasons)
# Number of species
N <- dim(phwhall_occobject$y)[1]
# Distances between sites
dist.phwh <- dist(phwhall_occobject$coords)
# Exponential covariance model
cov.model <- "exponential"
ms.inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(phwhall_occobject$y, c(1, 2), max, na.rm = TRUE))
ms.priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
out.alltax.allsigcor.ms <- msPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula,
det.formula = phwhsal_occobject_season.det.formula,
data = phwhall_occobject,
inits = ms.inits,
n.samples = 30000,
priors = ms.priors,
n.omp.threads = 4,
verbose = TRUE,
n.report = 60000,
n.burn = 10000,
n.thin = 50,
n.chains = 3)
summary(out.alltax.allsigcor.ms, level = 'both')
ppc.alltax.allsigcor.ms.out <- ppcOcc(out.alltax.allsigcor.ms, 'chi-squared', group = 1)
summary(ppc.alltax.allsigcor.ms.out, level = 'both')
waicOcc(out.alltax.allsigcor.ms)improved detection model
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_dayyearseason.det.formula <- ~scale(seasons)+scale(dayyear)
# Number of species
N <- dim(phwhall_occobject$y)[1]
# Distances between sites
dist.phwh <- dist(phwhall_occobject$coords)
# Exponential covariance model
cov.model <- "exponential"
ms.inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(phwhall_occobject$y, c(1, 2), max, na.rm = TRUE))
ms.priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
out.alltax.allsigcor.impdet.ms <- msPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula,
det.formula = phwhall_occobject_dayyearseason.det.formula,
data = phwhall_occobject,
inits = ms.inits,
n.samples = 30000,
priors = ms.priors,
n.omp.threads = 4,
verbose = TRUE,
n.report = 60000,
n.burn = 10000,
n.thin = 50,
n.chains = 3)
summary(out.alltax.allsigcor.impdet.ms, level = 'both')
ppc.alltax.allsigcor.impdet.ms.out <- ppcOcc(out.alltax.allsigcor.impdet.ms, 'chi-squared', group = 1)
summary(ppc.alltax.allsigcor.impdet.ms.out, level = 'both')
waicOcc(out.alltax.allsigcor.impdet.ms)
write_rds(out.alltax.allsigcor.impdet.ms,"alltaxmodel_phwh.rds")full null
null <- ~1
# Number of species
N <- dim(phwhall_occobject$y)[1]
# Distances between sites
dist.phwh <- dist(phwhall_occobject$coords)
# Exponential covariance model
cov.model <- "exponential"
ms.inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(phwhall_occobject$y, c(1, 2), max, na.rm = TRUE))
ms.priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
out.null <- msPGOcc(occ.formula = null,
det.formula = null,
data = phwhall_occobject,
inits = ms.inits,
n.samples = 30000,
priors = ms.priors,
n.omp.threads = 4,
verbose = TRUE,
n.report = 60000,
n.burn = 10000,
n.thin = 50,
n.chains = 3)
summary(out.null, level = 'both')
ppc.out.null <- ppcOcc(out.null, 'chi-squared', group = 1)
summary(ppc.out.null, level = 'both')
waicOcc(out.null)Will want to exclude species that did not fit as well.
out.alltax.allsigcor.impdet.ms<-readRDS("alltaxmodel_phwh.rds")
summary(out.alltax.allsigcor.impdet.ms,max.print=100000)##
## Call:
## msPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula, det.formula = phwhall_occobject_dayyearseason.det.formula, data = phwhall_occobject, inits = ms.inits, priors = ms.priors, n.samples = 30000, n.omp.threads = 4, verbose = TRUE, n.report = 60000, n.burn = 10000, n.thin = 50, n.chains = 3)
##
## Samples per Chain: 30000
## Burn-in: 10000
## Thinning Rate: 50
## Number of Chains: 3
## Total Posterior Samples: 1200
## Run Time (min): 52.2222
##
## ----------------------------------------
## Community Level
## ----------------------------------------
## Occurrence Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -2.8897 0.8106 -4.1358 -3.0216 -0.8177 1.3829 19
## scale(pool_depth_.cm.) -0.0189 0.1261 -0.2612 -0.0202 0.2446 1.0128 996
## scale(per_sand) 0.4350 0.0947 0.2540 0.4328 0.6232 1.0005 1264
## scale(per_cobble) 0.2455 0.0708 0.1068 0.2441 0.3797 1.0046 1200
## scale(substrate_metric_total) 0.3483 0.0759 0.1939 0.3484 0.4971 1.0016 1076
## scale(pool_metric_total) -0.0111 0.0659 -0.1415 -0.0112 0.1189 1.0040 1121
##
## Occurrence Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 20.1277 6.2804 6.2490 19.9050 33.7245 1.2347 39
## scale(pool_depth_.cm.) 0.6635 0.1997 0.3567 0.6368 1.1398 1.0009 846
## scale(per_sand) 0.2564 0.0786 0.1299 0.2473 0.4325 1.0016 1200
## scale(per_cobble) 0.0880 0.0414 0.0304 0.0807 0.1884 1.0074 874
## scale(substrate_metric_total) 0.0931 0.0465 0.0323 0.0825 0.2054 1.0020 1088
## scale(pool_metric_total) 0.0986 0.0432 0.0357 0.0911 0.2026 1.0064 1261
##
## Detection Means (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) -1.6660 0.6560 -3.5379 -1.5174 -0.8506 1.5221 15
## scale(seasons) -0.2099 0.0849 -0.3768 -0.2093 -0.0468 1.0000 1200
## scale(dayyear) 0.0430 0.0670 -0.0857 0.0408 0.1796 0.9997 1329
##
## Detection Variances (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept) 4.2489 2.8532 1.6422 3.4169 13.0521 1.8461 11
## scale(seasons) 0.3257 0.0801 0.1999 0.3122 0.5103 1.0039 1377
## scale(dayyear) 0.1949 0.0557 0.1075 0.1867 0.3228 1.0066 1200
##
## ----------------------------------------
## Species Level
## ----------------------------------------
## Occurrence (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept)-streambiotic_suff_mtdusky -1.6714 0.3158 -2.2931 -1.6676 -1.0738 1.0069 1200
## (Intercept)-streambiotic_suff_nodusky -0.2926 0.2490 -0.7606 -0.2946 0.2157 1.0019 1200
## (Intercept)-streambiotic_suff_twolined 1.3494 0.2331 0.9142 1.3450 1.8238 1.0085 1200
## (Intercept)-streambiotic_suff_longtail -2.7872 0.9864 -4.3067 -2.8829 -0.7278 1.0416 344
## (Intercept)-streambiotic_suff_red -2.1236 0.3988 -2.9360 -2.1125 -1.3427 1.0015 1200
## (Intercept)-streambiotic_suff_mole -2.8320 2.1500 -6.1460 -3.2241 2.8850 1.0121 87
## (Intercept)-streambiotic_suff_fourtoed -7.0579 3.5622 -14.7157 -6.9632 0.7699 1.1068 80
## (Intercept)-streambiotic_suff_redback 0.2688 0.2868 -0.2481 0.2551 0.8896 1.0116 1059
## (Intercept)-streambiotic_suff_slimy -2.6199 0.6411 -3.8349 -2.6258 -1.3496 1.0093 1200
## (Intercept)-blacknose_dace -1.2054 0.4735 -2.0231 -1.2426 -0.1994 1.0119 1200
## (Intercept)-bluegill_sunfish -3.7258 2.3377 -7.3622 -4.1296 2.0549 1.1298 79
## (Intercept)-bluntnose_minnow -6.8915 3.7733 -14.1115 -6.9540 2.4590 1.2352 70
## (Intercept)-brindled_madtom -7.0534 3.2793 -13.9572 -6.9878 0.3016 1.0210 111
## (Intercept)-brook_stickleback -7.1122 3.2834 -13.7670 -7.0479 0.1516 1.1848 123
## (Intercept)-brook_trout -7.1785 3.3522 -14.0238 -7.1069 -0.3317 1.1173 106
## (Intercept)-central_mudminnow -7.4677 3.1885 -14.4570 -7.2760 -1.3172 1.0883 88
## (Intercept)-central_stoneroller_minnow 0.2610 2.6950 -3.6953 -0.0168 6.3101 1.0429 153
## (Intercept)-common_shiner -3.7405 2.3847 -7.5003 -4.0724 2.3740 1.0714 145
## (Intercept)-creek_chub 0.2249 0.4715 -0.5601 0.1843 1.2506 1.0145 1200
## (Intercept)-eastern_sand_darter -7.1371 3.3839 -14.2057 -6.8568 -0.4857 1.1854 113
## (Intercept)-fantail_darter -7.3309 3.4109 -13.6478 -7.3197 0.2814 1.0633 74
## (Intercept)-fathead_minnow -7.0752 3.7493 -14.4893 -7.0690 1.8171 1.0938 78
## (Intercept)-golden_shiner -7.0745 3.4416 -14.1829 -7.0144 0.0641 1.1160 145
## (Intercept)-goldfish -6.7943 3.3873 -13.6019 -6.7761 0.4492 1.1215 93
## (Intercept)-grass_pickerel -7.0637 3.4071 -14.2590 -6.9380 -0.0003 1.0807 82
## (Intercept)-greenside_darter -7.0977 3.4632 -14.1352 -6.9934 0.4010 1.1293 112
## (Intercept)-green_sunfish -0.8145 2.8525 -5.0552 -1.3766 6.3994 1.0220 107
## (Intercept)-johnny_darter -1.1372 2.5547 -4.2377 -1.8215 5.8808 1.1016 89
## (Intercept)-largemouth_bass -7.0980 3.2337 -13.4376 -6.8887 -0.2490 1.0630 110
## (Intercept)-longear_sunfish -7.4140 3.2980 -14.9066 -7.0032 -1.7382 1.1130 144
## (Intercept)-longnose_dace -6.8785 3.2587 -13.3308 -6.8346 -0.2809 1.0859 127
## (Intercept)-mottled_sculpin -7.2003 3.2037 -14.1498 -6.9098 -0.5250 1.1300 111
## (Intercept)-northern_hog_sucker -6.8674 3.3454 -14.1051 -6.5850 -0.0535 1.0572 178
## (Intercept)-pumpkinseed_sunfish -7.1124 3.4247 -14.5798 -6.8443 -0.5439 1.1252 141
## (Intercept)-rainbow_darter -2.8952 1.3061 -4.8557 -3.1010 0.1429 1.0408 150
## (Intercept)-rainbow_trout -6.9916 3.3899 -14.2202 -6.8555 0.2404 1.0369 104
## (Intercept)-redbelly_dace -2.4819 2.2035 -5.7180 -2.9002 3.0511 1.0440 182
## (Intercept)-redside_dace -3.3292 2.4776 -7.2167 -3.7049 2.7117 1.0906 83
## (Intercept)-river_chub -6.8888 3.4570 -13.7146 -7.0238 1.8403 1.2002 85
## (Intercept)-silverjaw_minnow -7.4363 3.4387 -15.0933 -7.0283 -1.3192 1.1063 254
## (Intercept)-smallmouth_bass -7.1866 3.0710 -13.7327 -7.0961 -1.0083 1.1085 193
## (Intercept)-spotfin_shiner -7.0542 3.2570 -14.2424 -6.8545 -0.4446 1.1031 178
## (Intercept)-stonecat_madtom -7.2054 3.1927 -13.8734 -7.1343 -0.8378 1.1105 117
## (Intercept)-striped_shiner -7.1079 3.3263 -13.8537 -7.1191 0.0730 1.0957 77
## (Intercept)-trout_perch -7.0610 3.1221 -14.3304 -6.7461 -1.2735 1.0290 122
## (Intercept)-warmouth_sunfish -6.8193 3.3254 -13.0212 -6.8420 0.2631 1.0828 67
## (Intercept)-white_sucker -1.6229 2.0338 -4.3526 -2.0695 3.8386 1.0967 144
## (Intercept)-yellow_bullhead_catfish -3.4573 2.7157 -7.4249 -4.0641 3.5879 1.0420 82
## (Intercept)-sessile_animals -1.6096 2.0533 -4.3211 -2.1043 3.5089 1.0108 167
## (Intercept)-aquatic_worms 3.1021 0.6406 2.1854 3.0119 4.5790 1.0284 1003
## (Intercept)-sow_bugs -0.8697 0.2401 -1.3433 -0.8673 -0.4078 1.0136 1115
## (Intercept)-scuds -0.2922 0.2437 -0.7547 -0.3094 0.2216 1.0026 1200
## (Intercept)-water_mites 0.8918 0.6268 -0.0568 0.7870 2.3533 1.0032 1456
## (Intercept)-damselfly_nymphs 1.2825 0.4296 0.5423 1.2496 2.1956 1.0138 1200
## (Intercept)-alderfly_larvae 0.9982 0.5220 0.2616 0.9258 2.1611 1.0072 1200
## (Intercept)-other_beetles 2.3960 0.5824 1.5310 2.3072 3.7063 1.0123 1200
## (Intercept)-crayfish 1.7067 0.3134 1.1683 1.6755 2.3998 0.9998 1200
## (Intercept)-dragonfly_nymphs 2.1909 0.5235 1.3590 2.1165 3.3958 1.0131 1090
## (Intercept)-riffle_beetles 0.8138 0.3060 0.2840 0.7827 1.5518 1.0094 983
## (Intercept)-other_flies 5.3742 1.3067 3.5999 5.1581 8.4109 1.0189 335
## (Intercept)-midges 7.0682 1.8530 4.4088 6.7589 11.5066 1.0097 532
## (Intercept)-snails 2.2443 0.5670 1.3429 2.1747 3.4480 1.0238 884
## (Intercept)-clams 1.0285 1.2947 -0.2492 0.6834 4.8916 1.0049 387
## (Intercept)-fishfly_larvae 0.2736 0.3555 -0.3510 0.2447 1.0270 1.0014 1200
## (Intercept)-water_penny_beetles -0.3778 0.3146 -0.9740 -0.3852 0.2687 1.0134 1200
## (Intercept)-cranefly_larvae 3.5094 0.6563 2.4088 3.4274 4.9850 1.0090 1200
## (Intercept)-ameletidae 2.2774 1.2939 0.8079 1.9868 5.7393 1.0365 269
## (Intercept)-arthropleidae -6.9640 3.5273 -13.8095 -7.1280 1.2260 1.0377 79
## (Intercept)-baetidae 2.4801 0.4639 1.7742 2.4257 3.4994 1.0016 1200
## (Intercept)-baetiscidae -7.3073 3.3593 -14.5140 -7.0662 -0.9920 1.0949 197
## (Intercept)-caenidae -1.9179 0.4897 -2.8016 -1.9432 -0.9140 1.0055 1083
## (Intercept)-ephemerellidae 0.3600 2.6123 -3.2720 -0.0905 6.7463 1.0277 125
## (Intercept)-ephemeridae -0.9252 3.0957 -4.4288 -1.6916 7.7726 1.1372 96
## (Intercept)-heptageniidae -0.4805 0.3024 -1.0301 -0.4907 0.1644 1.0041 1200
## (Intercept)-isonychiidae -6.8291 3.4875 -13.2607 -6.9023 1.7498 1.0942 91
## (Intercept)-leptohyphidae 0.7116 2.4152 -2.1556 0.0602 6.7710 1.0148 175
## (Intercept)-leptophlebiidae -0.4340 0.2062 -0.8396 -0.4433 -0.0128 1.0010 1200
## (Intercept)-polymitarcyidae -7.0008 3.4940 -14.2122 -6.9362 1.1252 1.0595 135
## (Intercept)-potamanthidae -6.7182 3.5802 -14.0777 -6.6941 1.2172 1.0667 105
## (Intercept)-pseudironidae -7.2023 3.4675 -14.7931 -6.9349 -0.3905 1.2009 121
## (Intercept)-siphlonuridae -7.3228 3.2561 -13.7737 -7.0589 -0.8043 1.0723 81
## (Intercept)-capniidae -7.1588 3.3482 -14.1327 -7.2355 -0.5028 1.0430 111
## (Intercept)-chloroperlidae -0.8242 0.7049 -1.8431 -0.9236 0.8767 1.0383 784
## (Intercept)-leuctridae 0.0479 0.1864 -0.2760 0.0407 0.4252 1.0040 1200
## (Intercept)-nemouridae 0.2620 0.4565 -0.4265 0.2004 1.2542 1.0170 1281
## (Intercept)-peltoperlidae -6.8993 3.6425 -14.4221 -6.7738 0.4331 1.0945 155
## (Intercept)-perlidae -0.5157 0.2394 -0.9731 -0.5195 -0.0517 1.0021 1489
## (Intercept)-perlodidae 2.6890 2.3437 -0.3275 2.1992 8.1902 1.1052 259
## (Intercept)-pteronarcyidae -7.4993 3.2902 -15.0314 -7.1267 -1.1286 1.0375 232
## (Intercept)-taeniopterygidae -7.4550 3.0921 -13.7788 -7.3171 -1.5923 1.0829 163
## (Intercept)-brachycentridae -7.1579 3.0476 -13.6339 -7.0785 -0.9852 1.1407 102
## (Intercept)-dipseuodopsidae -7.0052 3.2048 -13.8019 -6.8476 -0.6929 1.0892 75
## (Intercept)-glossosomatidae -4.5174 0.9038 -6.4746 -4.4723 -2.9239 1.0120 1067
## (Intercept)-goeridae -7.1288 3.1598 -14.4513 -6.8562 -0.8039 1.1530 130
## (Intercept)-helicopsychidae -7.0960 3.3249 -14.0048 -6.9356 0.0352 1.0802 64
## (Intercept)-hydropsychidae 1.6188 0.2750 1.1073 1.6129 2.2024 1.0114 1200
## (Intercept)-hydroptilidae -1.1451 0.6431 -2.1779 -1.2053 0.3192 0.9996 888
## (Intercept)-lepidostomatidae -1.5017 0.2951 -2.1085 -1.4880 -0.9440 1.0006 1200
## (Intercept)-leptoceridae -7.3243 3.5354 -15.4596 -7.0670 -1.1162 1.1471 114
## (Intercept)-limnephilidae -0.4135 0.2696 -0.9207 -0.4217 0.1196 1.0106 1094
## (Intercept)-molannidae -3.7488 0.9263 -5.5739 -3.7192 -1.8769 1.0167 878
## (Intercept)-odontoceridae -3.0429 1.0115 -4.8007 -3.1240 -0.8342 1.0059 739
## (Intercept)-philopotamidae 0.6268 0.3545 0.0010 0.6167 1.3451 1.0047 1319
## (Intercept)-phryganeidae -1.2262 1.4137 -2.9907 -1.5792 2.7230 1.1993 217
## (Intercept)-polycentropodidae 0.1839 0.3187 -0.3635 0.1608 0.8722 1.0005 1345
## (Intercept)-psychomiidae -7.0248 3.5038 -13.3155 -7.0853 1.5167 1.1337 76
## (Intercept)-rhyacophilidae -1.1545 0.3760 -1.8427 -1.1751 -0.3484 1.0020 1257
## (Intercept)-uenoidae 0.3959 0.5554 -0.4218 0.3158 1.7402 1.0068 936
## scale(pool_depth_.cm.)-streambiotic_suff_mtdusky -0.5721 0.3134 -1.2483 -0.5514 -0.0350 1.0033 1200
## scale(pool_depth_.cm.)-streambiotic_suff_nodusky -0.7842 0.2582 -1.2984 -0.7789 -0.3103 1.0140 1075
## scale(pool_depth_.cm.)-streambiotic_suff_twolined -0.6604 0.2088 -1.0743 -0.6595 -0.2790 1.0029 1091
## scale(pool_depth_.cm.)-streambiotic_suff_longtail -0.6566 0.5489 -1.7810 -0.6349 0.3933 1.0415 1200
## scale(pool_depth_.cm.)-streambiotic_suff_red -1.0977 0.3982 -1.9069 -1.0867 -0.3832 1.0115 1200
## scale(pool_depth_.cm.)-streambiotic_suff_mole -0.9076 0.7176 -2.3994 -0.8869 0.4946 1.0021 988
## scale(pool_depth_.cm.)-streambiotic_suff_fourtoed -0.0487 0.7615 -1.5535 -0.0615 1.4115 0.9997 1200
## scale(pool_depth_.cm.)-streambiotic_suff_redback -1.0551 0.2835 -1.6449 -1.0433 -0.5179 1.0013 1200
## scale(pool_depth_.cm.)-streambiotic_suff_slimy -0.9919 0.5187 -2.1346 -0.9572 -0.0802 1.0105 1200
## scale(pool_depth_.cm.)-blacknose_dace 0.5650 0.3711 -0.0953 0.5400 1.3433 1.0156 1200
## scale(pool_depth_.cm.)-bluegill_sunfish -0.3383 0.7034 -1.7435 -0.3499 1.1214 1.0002 1200
## scale(pool_depth_.cm.)-bluntnose_minnow -0.0500 0.7763 -1.5662 -0.0645 1.4439 1.0062 1200
## scale(pool_depth_.cm.)-brindled_madtom -0.0269 0.7692 -1.5234 -0.0294 1.4443 1.0059 1200
## scale(pool_depth_.cm.)-brook_stickleback 0.0159 0.7945 -1.5287 -0.0205 1.6555 1.0075 1200
## scale(pool_depth_.cm.)-brook_trout 0.0087 0.7640 -1.5761 0.0447 1.4683 1.0095 1200
## scale(pool_depth_.cm.)-central_mudminnow -0.0329 0.7828 -1.5765 -0.0154 1.4825 1.0007 1200
## scale(pool_depth_.cm.)-central_stoneroller_minnow 0.2590 0.8199 -1.3787 0.2667 1.8791 1.0101 1086
## scale(pool_depth_.cm.)-common_shiner 0.6191 0.6970 -0.7559 0.6007 2.1389 1.0067 991
## scale(pool_depth_.cm.)-creek_chub 1.1814 0.4757 0.3622 1.1438 2.2180 1.0068 1200
## scale(pool_depth_.cm.)-eastern_sand_darter -0.0521 0.7610 -1.4936 -0.0424 1.4474 1.0035 1200
## scale(pool_depth_.cm.)-fantail_darter 0.0147 0.7999 -1.5982 0.0380 1.5339 1.0040 1200
## scale(pool_depth_.cm.)-fathead_minnow -0.0331 0.7906 -1.5579 -0.0101 1.5230 1.0003 1200
## scale(pool_depth_.cm.)-golden_shiner -0.0539 0.8068 -1.6405 -0.0610 1.5348 1.0050 1200
## scale(pool_depth_.cm.)-goldfish -0.0058 0.7821 -1.5214 -0.0093 1.5780 1.0121 1200
## scale(pool_depth_.cm.)-grass_pickerel -0.0423 0.7850 -1.6429 -0.0157 1.4344 1.0009 1200
## scale(pool_depth_.cm.)-greenside_darter -0.0121 0.7439 -1.4593 -0.0037 1.4616 1.0025 1200
## scale(pool_depth_.cm.)-green_sunfish 0.2618 0.7810 -1.2521 0.2475 1.7928 1.0068 936
## scale(pool_depth_.cm.)-johnny_darter 0.6618 0.6734 -0.8229 0.6631 1.9769 1.0001 520
## scale(pool_depth_.cm.)-largemouth_bass -0.0300 0.7910 -1.5812 -0.0329 1.5342 1.0052 1200
## scale(pool_depth_.cm.)-longear_sunfish -0.0159 0.7591 -1.4462 -0.0303 1.4447 1.0018 1200
## scale(pool_depth_.cm.)-longnose_dace 0.0036 0.7646 -1.4051 0.0036 1.5188 1.0004 1587
## scale(pool_depth_.cm.)-mottled_sculpin -0.0119 0.7899 -1.5727 -0.0110 1.4861 1.0066 1200
## scale(pool_depth_.cm.)-northern_hog_sucker -0.0127 0.7867 -1.6250 0.0253 1.4802 1.0018 1008
## scale(pool_depth_.cm.)-pumpkinseed_sunfish -0.0756 0.7903 -1.6087 -0.0839 1.5422 1.0034 1081
## scale(pool_depth_.cm.)-rainbow_darter 0.7779 0.5835 -0.2323 0.7499 2.0978 1.0073 913
## scale(pool_depth_.cm.)-rainbow_trout -0.0684 0.7989 -1.7436 -0.0315 1.3929 1.0024 1200
## scale(pool_depth_.cm.)-redbelly_dace 0.7809 0.7196 -0.7193 0.7756 2.2018 1.0162 686
## scale(pool_depth_.cm.)-redside_dace -0.2126 0.7060 -1.6647 -0.2060 1.1618 1.0062 1099
## scale(pool_depth_.cm.)-river_chub -0.0533 0.7839 -1.5771 -0.0474 1.3524 1.0155 1200
## scale(pool_depth_.cm.)-silverjaw_minnow -0.0356 0.8094 -1.6096 -0.0248 1.5726 1.0039 1200
## scale(pool_depth_.cm.)-smallmouth_bass 0.0119 0.7873 -1.5953 0.0186 1.5768 1.0031 1414
## scale(pool_depth_.cm.)-spotfin_shiner 0.0239 0.7455 -1.3624 0.0188 1.5160 1.0024 1200
## scale(pool_depth_.cm.)-stonecat_madtom -0.0216 0.7837 -1.5568 -0.0101 1.5292 1.0052 1200
## scale(pool_depth_.cm.)-striped_shiner -0.0399 0.7729 -1.5243 -0.0367 1.5115 1.0008 1078
## scale(pool_depth_.cm.)-trout_perch -0.0278 0.7578 -1.4970 -0.0269 1.4849 1.0027 1663
## scale(pool_depth_.cm.)-warmouth_sunfish -0.0519 0.7558 -1.6268 -0.0662 1.3638 1.0097 1033
## scale(pool_depth_.cm.)-white_sucker 0.6162 0.6370 -0.6688 0.5890 1.8982 1.0058 1059
## scale(pool_depth_.cm.)-yellow_bullhead_catfish 0.0253 0.7096 -1.4565 0.0496 1.3573 1.0042 1200
## scale(pool_depth_.cm.)-sessile_animals -0.2776 0.6718 -1.6544 -0.2773 1.0764 1.0024 1010
## scale(pool_depth_.cm.)-aquatic_worms 0.3812 0.4817 -0.4233 0.3384 1.4015 1.0017 1200
## scale(pool_depth_.cm.)-sow_bugs 0.7583 0.2480 0.2959 0.7456 1.2735 1.0006 1200
## scale(pool_depth_.cm.)-scuds 0.1082 0.2236 -0.3420 0.0993 0.5576 1.0075 1327
## scale(pool_depth_.cm.)-water_mites 0.3881 0.3893 -0.3068 0.3471 1.2303 1.0022 1200
## scale(pool_depth_.cm.)-damselfly_nymphs 1.8763 0.5027 1.0259 1.8276 2.9761 1.0142 1089
## scale(pool_depth_.cm.)-alderfly_larvae 0.0138 0.3669 -0.6618 -0.0023 0.8137 1.0031 1200
## scale(pool_depth_.cm.)-other_beetles -0.4390 0.4387 -1.2307 -0.4436 0.4510 1.0049 1200
## scale(pool_depth_.cm.)-crayfish -0.7319 0.2800 -1.3096 -0.7266 -0.2157 1.0027 1260
## scale(pool_depth_.cm.)-dragonfly_nymphs 0.5327 0.4620 -0.2877 0.4973 1.5345 1.0090 1200
## scale(pool_depth_.cm.)-riffle_beetles 0.6434 0.3300 0.1102 0.6021 1.3725 1.0012 1200
## scale(pool_depth_.cm.)-other_flies 0.6445 0.6702 -0.5878 0.6266 2.0818 1.0067 1200
## scale(pool_depth_.cm.)-midges 0.0055 0.7013 -1.3490 -0.0034 1.4121 1.0017 1155
## scale(pool_depth_.cm.)-snails 1.3655 0.5142 0.5131 1.3260 2.5663 1.0111 1200
## scale(pool_depth_.cm.)-clams 0.2600 0.5150 -0.7002 0.2306 1.4267 1.0055 1200
## scale(pool_depth_.cm.)-fishfly_larvae -0.8439 0.2902 -1.4699 -0.8277 -0.3167 1.0035 1200
## scale(pool_depth_.cm.)-water_penny_beetles -0.4621 0.2786 -1.0124 -0.4561 0.0523 1.0020 1200
## scale(pool_depth_.cm.)-cranefly_larvae 0.5872 0.5421 -0.3626 0.5409 1.7337 0.9994 1200
## scale(pool_depth_.cm.)-ameletidae 1.0107 0.6725 -0.3677 0.9859 2.3581 1.0013 862
## scale(pool_depth_.cm.)-arthropleidae -0.0337 0.7990 -1.6697 -0.0473 1.4618 1.0025 1200
## scale(pool_depth_.cm.)-baetidae 0.3418 0.4131 -0.3347 0.2815 1.3211 1.0016 1200
## scale(pool_depth_.cm.)-baetiscidae -0.0522 0.7918 -1.5842 -0.0761 1.5067 0.9999 1200
## scale(pool_depth_.cm.)-caenidae 0.5639 0.3012 -0.0242 0.5677 1.1699 1.0028 1200
## scale(pool_depth_.cm.)-ephemerellidae 0.0922 0.7083 -1.2929 0.0837 1.5920 1.0257 896
## scale(pool_depth_.cm.)-ephemeridae 0.3737 0.7191 -1.0703 0.3908 1.7517 1.0599 437
## scale(pool_depth_.cm.)-heptageniidae 0.1435 0.2627 -0.3434 0.1349 0.7013 1.0072 1200
## scale(pool_depth_.cm.)-isonychiidae -0.0171 0.7990 -1.4913 -0.0128 1.5263 1.0148 1200
## scale(pool_depth_.cm.)-leptohyphidae 0.2254 0.6375 -1.0456 0.2255 1.5010 1.0010 961
## scale(pool_depth_.cm.)-leptophlebiidae -0.6252 0.2246 -1.0706 -0.6316 -0.1991 1.0000 1200
## scale(pool_depth_.cm.)-polymitarcyidae -0.0425 0.7530 -1.6391 -0.0277 1.3684 0.9996 1200
## scale(pool_depth_.cm.)-potamanthidae -0.0035 0.7371 -1.4756 0.0067 1.3912 1.0077 1019
## scale(pool_depth_.cm.)-pseudironidae -0.0533 0.7666 -1.5151 -0.0846 1.4163 1.0036 1588
## scale(pool_depth_.cm.)-siphlonuridae -0.0267 0.7894 -1.6992 -0.0062 1.5015 1.0188 1200
## scale(pool_depth_.cm.)-capniidae -0.0111 0.8059 -1.6136 -0.0286 1.6281 1.0008 1200
## scale(pool_depth_.cm.)-chloroperlidae 0.0537 0.3691 -0.6651 0.0521 0.7682 1.0088 1200
## scale(pool_depth_.cm.)-leuctridae -0.4966 0.1975 -0.8866 -0.4872 -0.1428 1.0049 1200
## scale(pool_depth_.cm.)-nemouridae -0.3199 0.2910 -0.8798 -0.3130 0.2642 1.0083 1413
## scale(pool_depth_.cm.)-peltoperlidae -0.0431 0.7812 -1.5270 -0.0546 1.4684 1.0019 1200
## scale(pool_depth_.cm.)-perlidae -0.1929 0.2341 -0.6473 -0.1832 0.2510 1.0052 1200
## scale(pool_depth_.cm.)-perlodidae -0.0858 0.7054 -1.4198 -0.1060 1.3162 1.0073 1200
## scale(pool_depth_.cm.)-pteronarcyidae 0.0240 0.8037 -1.6344 0.0330 1.5746 1.0000 1141
## scale(pool_depth_.cm.)-taeniopterygidae -0.0250 0.7846 -1.5473 -0.0326 1.5411 1.0029 1200
## scale(pool_depth_.cm.)-brachycentridae -0.0164 0.7713 -1.4592 -0.0315 1.5209 1.0018 1200
## scale(pool_depth_.cm.)-dipseuodopsidae -0.0214 0.7743 -1.5572 -0.0097 1.4623 1.0083 1200
## scale(pool_depth_.cm.)-glossosomatidae -0.6362 0.6297 -1.8978 -0.6173 0.5193 1.0033 1200
## scale(pool_depth_.cm.)-goeridae -0.0209 0.7837 -1.5303 -0.0215 1.5492 1.0031 1200
## scale(pool_depth_.cm.)-helicopsychidae -0.0834 0.7991 -1.6098 -0.0707 1.4864 0.9991 1200
## scale(pool_depth_.cm.)-hydropsychidae -0.1457 0.2545 -0.6179 -0.1557 0.3428 1.0010 1330
## scale(pool_depth_.cm.)-hydroptilidae 0.5170 0.4096 -0.2662 0.4934 1.3736 0.9999 1048
## scale(pool_depth_.cm.)-lepidostomatidae -1.2124 0.3276 -1.8826 -1.2014 -0.6162 1.0022 1200
## scale(pool_depth_.cm.)-leptoceridae -0.0574 0.7597 -1.5809 -0.0336 1.4168 1.0045 1200
## scale(pool_depth_.cm.)-limnephilidae -0.3823 0.2455 -0.8713 -0.3832 0.1195 1.0007 1200
## scale(pool_depth_.cm.)-molannidae -0.8066 0.5620 -1.9310 -0.7845 0.2203 1.0068 1200
## scale(pool_depth_.cm.)-odontoceridae -1.0337 0.5910 -2.2649 -1.0067 0.0531 1.0009 1200
## scale(pool_depth_.cm.)-philopotamidae -0.2216 0.2847 -0.7565 -0.2316 0.3767 1.0077 1200
## scale(pool_depth_.cm.)-phryganeidae 0.5172 0.5112 -0.5364 0.5075 1.5256 1.0056 1206
## scale(pool_depth_.cm.)-polycentropodidae -0.2271 0.2569 -0.7201 -0.2275 0.3022 1.0063 1200
## scale(pool_depth_.cm.)-psychomiidae -0.0645 0.8020 -1.6344 -0.0888 1.4950 1.0040 1095
## scale(pool_depth_.cm.)-rhyacophilidae -0.9063 0.3370 -1.5664 -0.9061 -0.2724 1.0123 1200
## scale(pool_depth_.cm.)-uenoidae -0.2066 0.3342 -0.8491 -0.2077 0.4331 1.0007 871
## scale(per_sand)-streambiotic_suff_mtdusky -0.2264 0.2911 -0.8122 -0.2106 0.3253 1.0001 1094
## scale(per_sand)-streambiotic_suff_nodusky 0.0764 0.2511 -0.4136 0.0780 0.5776 1.0092 1200
## scale(per_sand)-streambiotic_suff_twolined 0.5868 0.2488 0.0971 0.5730 1.0860 1.0005 1200
## scale(per_sand)-streambiotic_suff_longtail 0.9938 0.3939 0.2583 0.9768 1.8217 1.0217 1200
## scale(per_sand)-streambiotic_suff_red 0.7818 0.3008 0.1956 0.7856 1.3931 1.0005 1200
## scale(per_sand)-streambiotic_suff_mole 0.5087 0.4496 -0.3977 0.5194 1.3751 1.0108 1200
## scale(per_sand)-streambiotic_suff_fourtoed 0.4138 0.5095 -0.5593 0.4133 1.4531 1.0008 1200
## scale(per_sand)-streambiotic_suff_redback 0.5509 0.2737 0.0488 0.5419 1.1473 1.0073 1200
## scale(per_sand)-streambiotic_suff_slimy 0.1019 0.3420 -0.5723 0.1071 0.7768 1.0026 1200
## scale(per_sand)-blacknose_dace 0.3719 0.3024 -0.2042 0.3691 0.9868 1.0048 1200
## scale(per_sand)-bluegill_sunfish 0.5103 0.4407 -0.3121 0.5010 1.4121 1.0033 1200
## scale(per_sand)-bluntnose_minnow 0.4056 0.4889 -0.5598 0.4052 1.3846 1.0034 1200
## scale(per_sand)-brindled_madtom 0.4121 0.4742 -0.5609 0.3938 1.3215 1.0053 1200
## scale(per_sand)-brook_stickleback 0.4091 0.5302 -0.6263 0.4269 1.3875 1.0053 1200
## scale(per_sand)-brook_trout 0.4423 0.5107 -0.5479 0.4420 1.4578 1.0021 1200
## scale(per_sand)-central_mudminnow 0.4218 0.5052 -0.5463 0.4132 1.4233 1.0197 1200
## scale(per_sand)-central_stoneroller_minnow 0.4118 0.4946 -0.5461 0.4049 1.3742 1.0018 1200
## scale(per_sand)-common_shiner 0.4871 0.4561 -0.4321 0.4938 1.3878 1.0074 1200
## scale(per_sand)-creek_chub 0.7388 0.3414 0.1218 0.7326 1.4541 0.9997 1200
## scale(per_sand)-eastern_sand_darter 0.4177 0.5066 -0.5574 0.4104 1.4417 1.0008 1200
## scale(per_sand)-fantail_darter 0.4235 0.5119 -0.6170 0.4133 1.4359 1.0045 1357
## scale(per_sand)-fathead_minnow 0.4188 0.5067 -0.6065 0.4230 1.3993 1.0011 1200
## scale(per_sand)-golden_shiner 0.4203 0.4975 -0.6039 0.4157 1.3731 1.0046 1302
## scale(per_sand)-goldfish 0.4414 0.4965 -0.5363 0.4422 1.4019 1.0027 1200
## scale(per_sand)-grass_pickerel 0.4418 0.4972 -0.5322 0.4638 1.4439 1.0071 1200
## scale(per_sand)-greenside_darter 0.4264 0.5048 -0.6079 0.4329 1.3943 1.0072 1200
## scale(per_sand)-green_sunfish 0.3924 0.4901 -0.5676 0.3997 1.3603 1.0062 1311
## scale(per_sand)-johnny_darter 0.5846 0.4425 -0.2272 0.5649 1.5239 1.0143 1166
## scale(per_sand)-largemouth_bass 0.4627 0.4960 -0.4644 0.4616 1.4421 1.0219 1200
## scale(per_sand)-longear_sunfish 0.4246 0.5035 -0.5009 0.4068 1.4500 1.0023 1216
## scale(per_sand)-longnose_dace 0.4317 0.5127 -0.5618 0.4617 1.4691 1.0093 1304
## scale(per_sand)-mottled_sculpin 0.4090 0.4818 -0.5461 0.4093 1.3157 1.0084 1200
## scale(per_sand)-northern_hog_sucker 0.4336 0.4756 -0.5308 0.4515 1.3549 1.0178 1200
## scale(per_sand)-pumpkinseed_sunfish 0.4175 0.5218 -0.6736 0.4465 1.4078 1.0029 1200
## scale(per_sand)-rainbow_darter 0.4073 0.4190 -0.3999 0.3965 1.2279 1.0009 1044
## scale(per_sand)-rainbow_trout 0.4146 0.4805 -0.6306 0.4292 1.3565 0.9997 1200
## scale(per_sand)-redbelly_dace 0.5144 0.4707 -0.3727 0.5069 1.5209 1.0004 1200
## scale(per_sand)-redside_dace 0.3104 0.4836 -0.6842 0.3253 1.2207 1.0038 1200
## scale(per_sand)-river_chub 0.4473 0.4831 -0.5082 0.4403 1.4010 1.0101 1359
## scale(per_sand)-silverjaw_minnow 0.4407 0.4963 -0.5138 0.4542 1.3854 1.0050 1200
## scale(per_sand)-smallmouth_bass 0.4376 0.5023 -0.5903 0.4490 1.3787 1.0005 1200
## scale(per_sand)-spotfin_shiner 0.4242 0.5009 -0.5736 0.4362 1.3842 1.0009 1200
## scale(per_sand)-stonecat_madtom 0.4334 0.4988 -0.5590 0.4225 1.4102 1.0111 1200
## scale(per_sand)-striped_shiner 0.4016 0.5002 -0.5712 0.4039 1.3859 1.0024 1200
## scale(per_sand)-trout_perch 0.4289 0.4835 -0.5544 0.4447 1.3575 1.0055 1076
## scale(per_sand)-warmouth_sunfish 0.4432 0.5062 -0.5711 0.4310 1.4665 1.0029 1338
## scale(per_sand)-white_sucker 0.5151 0.4279 -0.3694 0.5150 1.3285 1.0015 1200
## scale(per_sand)-yellow_bullhead_catfish 0.4388 0.4785 -0.5441 0.4452 1.3723 1.0054 1200
## scale(per_sand)-sessile_animals 0.1126 0.4469 -0.7989 0.1296 0.9526 1.0073 924
## scale(per_sand)-aquatic_worms -0.2854 0.3430 -0.9421 -0.2814 0.4034 1.0033 998
## scale(per_sand)-sow_bugs -0.3152 0.2827 -0.8839 -0.3137 0.2049 1.0005 1200
## scale(per_sand)-scuds -0.2759 0.2477 -0.7776 -0.2738 0.1976 1.0006 1200
## scale(per_sand)-water_mites 0.0189 0.3487 -0.7002 0.0308 0.6561 1.0157 1200
## scale(per_sand)-damselfly_nymphs 0.2651 0.2644 -0.2546 0.2606 0.7813 0.9996 1200
## scale(per_sand)-alderfly_larvae 0.3270 0.3188 -0.2681 0.3158 0.9929 1.0062 1200
## scale(per_sand)-other_beetles 0.8350 0.3689 0.1277 0.8363 1.5474 1.0032 1200
## scale(per_sand)-crayfish 0.5849 0.2884 0.0297 0.5758 1.1720 1.0018 1200
## scale(per_sand)-dragonfly_nymphs 0.6911 0.3229 0.1012 0.6775 1.3297 1.0079 1200
## scale(per_sand)-riffle_beetles 0.1993 0.2258 -0.2208 0.2007 0.6714 1.0171 1590
## scale(per_sand)-other_flies 0.4657 0.4521 -0.4179 0.4675 1.3712 1.0127 1200
## scale(per_sand)-midges 0.4480 0.4793 -0.5386 0.4581 1.3782 0.9995 1200
## scale(per_sand)-snails -0.3513 0.2871 -0.8953 -0.3510 0.2124 1.0001 1200
## scale(per_sand)-clams 0.3670 0.3779 -0.2849 0.3340 1.1881 1.0019 1200
## scale(per_sand)-fishfly_larvae 0.7447 0.2813 0.2188 0.7299 1.3135 1.0181 948
## scale(per_sand)-water_penny_beetles 0.1369 0.2805 -0.3955 0.1333 0.6889 1.0036 1200
## scale(per_sand)-cranefly_larvae 0.6893 0.3877 -0.0208 0.6868 1.4820 1.0003 1200
## scale(per_sand)-ameletidae 0.4053 0.3774 -0.3040 0.3997 1.1487 1.0075 1085
## scale(per_sand)-arthropleidae 0.4310 0.5072 -0.5733 0.4496 1.3745 1.0012 1200
## scale(per_sand)-baetidae 0.5222 0.3050 -0.0630 0.5167 1.1164 1.0101 1183
## scale(per_sand)-baetiscidae 0.4230 0.5045 -0.5693 0.4307 1.4127 1.0013 1200
## scale(per_sand)-caenidae 0.0729 0.3314 -0.5926 0.0685 0.7094 1.0101 1200
## scale(per_sand)-ephemerellidae 0.4391 0.4521 -0.3905 0.4285 1.3547 1.0001 1200
## scale(per_sand)-ephemeridae 0.3995 0.4648 -0.5196 0.3912 1.3470 1.0002 1200
## scale(per_sand)-heptageniidae 0.9981 0.2805 0.4550 0.9916 1.5394 1.0002 1200
## scale(per_sand)-isonychiidae 0.4259 0.5026 -0.5308 0.4288 1.4295 0.9998 1200
## scale(per_sand)-leptohyphidae 0.4154 0.4248 -0.3878 0.3990 1.3066 1.0010 1200
## scale(per_sand)-leptophlebiidae 0.8374 0.2356 0.3617 0.8329 1.3023 1.0073 1200
## scale(per_sand)-polymitarcyidae 0.4209 0.4927 -0.6083 0.4220 1.3947 1.0059 1200
## scale(per_sand)-potamanthidae 0.4117 0.5066 -0.5564 0.4154 1.4584 1.0013 1314
## scale(per_sand)-pseudironidae 0.4339 0.4975 -0.5414 0.4337 1.3759 1.0148 1200
## scale(per_sand)-siphlonuridae 0.4443 0.4878 -0.5034 0.4442 1.4051 1.0031 1200
## scale(per_sand)-capniidae 0.4126 0.5041 -0.5611 0.4120 1.3949 1.0073 1200
## scale(per_sand)-chloroperlidae 0.2650 0.3370 -0.4347 0.2569 0.9450 1.0112 1200
## scale(per_sand)-leuctridae 0.7323 0.2349 0.2971 0.7245 1.2031 1.0011 1200
## scale(per_sand)-nemouridae 0.8881 0.2944 0.3437 0.8754 1.5044 1.0021 1200
## scale(per_sand)-peltoperlidae 0.3803 0.4894 -0.6111 0.4016 1.3324 1.0057 1200
## scale(per_sand)-perlidae 0.8623 0.2716 0.3528 0.8611 1.4229 1.0043 1200
## scale(per_sand)-perlodidae 0.5867 0.4992 -0.3924 0.5914 1.5774 1.0030 952
## scale(per_sand)-pteronarcyidae 0.4276 0.5048 -0.5926 0.4236 1.3668 1.0071 1208
## scale(per_sand)-taeniopterygidae 0.4142 0.5015 -0.5777 0.4203 1.4237 1.0106 1200
## scale(per_sand)-brachycentridae 0.4226 0.4889 -0.5346 0.4331 1.4079 1.0214 1093
## scale(per_sand)-dipseuodopsidae 0.4190 0.5076 -0.5717 0.4296 1.3842 1.0012 1200
## scale(per_sand)-glossosomatidae 0.4833 0.3996 -0.3489 0.4973 1.2490 1.0059 1048
## scale(per_sand)-goeridae 0.4096 0.4949 -0.5589 0.4134 1.3566 1.0016 1200
## scale(per_sand)-helicopsychidae 0.4216 0.5058 -0.6244 0.4176 1.4687 1.0031 1200
## scale(per_sand)-hydropsychidae 0.5641 0.2746 0.0347 0.5500 1.1281 1.0025 1200
## scale(per_sand)-hydroptilidae -0.1727 0.3647 -0.9170 -0.1516 0.4719 0.9996 1200
## scale(per_sand)-lepidostomatidae 0.9913 0.2931 0.4282 0.9753 1.5907 1.0035 1200
## scale(per_sand)-leptoceridae 0.4135 0.5021 -0.6246 0.4374 1.3967 1.0031 1200
## scale(per_sand)-limnephilidae 1.3057 0.2943 0.7482 1.3006 1.8938 1.0042 1253
## scale(per_sand)-molannidae 1.0807 0.4147 0.3414 1.0634 1.9436 1.0010 1174
## scale(per_sand)-odontoceridae 0.6791 0.4039 -0.1066 0.6956 1.4446 1.0077 1005
## scale(per_sand)-philopotamidae 0.1392 0.2872 -0.4093 0.1399 0.7082 1.0032 1200
## scale(per_sand)-phryganeidae 0.6082 0.3787 -0.0924 0.5865 1.3492 1.0100 1200
## scale(per_sand)-polycentropodidae -0.0773 0.2572 -0.5720 -0.0763 0.4061 1.0093 1200
## scale(per_sand)-psychomiidae 0.4480 0.4959 -0.5192 0.4550 1.4066 1.0073 1200
## scale(per_sand)-rhyacophilidae 0.5940 0.2963 0.0334 0.5988 1.1805 1.0053 1200
## scale(per_sand)-uenoidae 0.6041 0.3371 -0.0312 0.5862 1.3118 0.9998 1200
## scale(per_cobble)-streambiotic_suff_mtdusky 0.2062 0.1964 -0.1551 0.2026 0.6083 0.9992 1200
## scale(per_cobble)-streambiotic_suff_nodusky 0.2514 0.2027 -0.1441 0.2465 0.6383 1.0040 1451
## scale(per_cobble)-streambiotic_suff_twolined 0.6377 0.2262 0.2251 0.6300 1.1214 1.0011 1200
## scale(per_cobble)-streambiotic_suff_longtail 0.2641 0.2777 -0.2735 0.2625 0.8261 1.0023 1082
## scale(per_cobble)-streambiotic_suff_red 0.3887 0.2321 -0.0409 0.3738 0.8462 0.9989 1200
## scale(per_cobble)-streambiotic_suff_mole 0.0957 0.3008 -0.5503 0.1106 0.6574 1.0031 1200
## scale(per_cobble)-streambiotic_suff_fourtoed 0.2508 0.3004 -0.3601 0.2520 0.8629 1.0021 1277
## scale(per_cobble)-streambiotic_suff_redback 0.0967 0.2092 -0.3371 0.1025 0.5098 1.0029 1200
## scale(per_cobble)-streambiotic_suff_slimy 0.2399 0.2536 -0.2735 0.2363 0.7532 1.0014 1200
## scale(per_cobble)-blacknose_dace 0.4232 0.2335 -0.0041 0.4171 0.9034 0.9996 1339
## scale(per_cobble)-bluegill_sunfish 0.2216 0.3036 -0.3827 0.2223 0.7852 1.0077 1200
## scale(per_cobble)-bluntnose_minnow 0.2517 0.2970 -0.3294 0.2604 0.8374 1.0063 1200
## scale(per_cobble)-brindled_madtom 0.2420 0.3007 -0.3762 0.2377 0.8490 1.0079 1097
## scale(per_cobble)-brook_stickleback 0.2401 0.3031 -0.3625 0.2326 0.8237 1.0130 1200
## scale(per_cobble)-brook_trout 0.2556 0.3060 -0.3358 0.2564 0.8287 1.0109 1200
## scale(per_cobble)-central_mudminnow 0.2352 0.2874 -0.3504 0.2293 0.8335 1.0026 1200
## scale(per_cobble)-central_stoneroller_minnow 0.1895 0.3071 -0.4393 0.1957 0.8073 1.0191 1200
## scale(per_cobble)-common_shiner 0.2403 0.3052 -0.3550 0.2474 0.8368 1.0040 1200
## scale(per_cobble)-creek_chub 0.1453 0.2286 -0.3235 0.1420 0.5688 1.0007 1200
## scale(per_cobble)-eastern_sand_darter 0.2360 0.3060 -0.3821 0.2480 0.8214 1.0081 1118
## scale(per_cobble)-fantail_darter 0.2482 0.2963 -0.3202 0.2511 0.8432 1.0029 1200
## scale(per_cobble)-fathead_minnow 0.2468 0.2954 -0.3387 0.2486 0.8532 1.0105 1200
## scale(per_cobble)-golden_shiner 0.2425 0.3010 -0.3579 0.2424 0.8288 0.9993 1200
## scale(per_cobble)-goldfish 0.2400 0.3036 -0.3910 0.2461 0.8446 1.0025 1200
## scale(per_cobble)-grass_pickerel 0.2462 0.2981 -0.3397 0.2458 0.8531 1.0041 1461
## scale(per_cobble)-greenside_darter 0.2454 0.2955 -0.3229 0.2449 0.8210 1.0109 1200
## scale(per_cobble)-green_sunfish 0.2223 0.2927 -0.3589 0.2216 0.8064 1.0013 1200
## scale(per_cobble)-johnny_darter 0.2600 0.2796 -0.3064 0.2643 0.8059 0.9999 1380
## scale(per_cobble)-largemouth_bass 0.2518 0.3057 -0.3530 0.2563 0.8448 1.0021 1050
## scale(per_cobble)-longear_sunfish 0.2464 0.2930 -0.3206 0.2473 0.8288 1.0069 1200
## scale(per_cobble)-longnose_dace 0.2380 0.2978 -0.3553 0.2252 0.8367 1.0038 1409
## scale(per_cobble)-mottled_sculpin 0.2407 0.2979 -0.3862 0.2444 0.8245 1.0123 1200
## scale(per_cobble)-northern_hog_sucker 0.2519 0.2940 -0.3489 0.2554 0.8149 1.0113 1200
## scale(per_cobble)-pumpkinseed_sunfish 0.2425 0.3074 -0.3742 0.2473 0.8135 0.9997 773
## scale(per_cobble)-rainbow_darter 0.3798 0.2697 -0.1299 0.3749 0.9059 1.0026 1200
## scale(per_cobble)-rainbow_trout 0.2469 0.3012 -0.3654 0.2474 0.8462 1.0013 1200
## scale(per_cobble)-redbelly_dace 0.2422 0.2784 -0.2978 0.2404 0.8065 1.0100 1037
## scale(per_cobble)-redside_dace 0.3638 0.3082 -0.1716 0.3554 0.9912 1.0013 839
## scale(per_cobble)-river_chub 0.2558 0.2950 -0.3058 0.2578 0.8440 1.0052 1200
## scale(per_cobble)-silverjaw_minnow 0.2367 0.3040 -0.3931 0.2421 0.8100 1.0033 1200
## scale(per_cobble)-smallmouth_bass 0.2488 0.3062 -0.3325 0.2466 0.8879 1.0120 1200
## scale(per_cobble)-spotfin_shiner 0.2491 0.2947 -0.3586 0.2578 0.8247 0.9998 1200
## scale(per_cobble)-stonecat_madtom 0.2500 0.2930 -0.3655 0.2457 0.8602 1.0028 1469
## scale(per_cobble)-striped_shiner 0.2480 0.2993 -0.3642 0.2443 0.8392 1.0093 1200
## scale(per_cobble)-trout_perch 0.2548 0.2905 -0.3178 0.2554 0.8109 1.0013 1591
## scale(per_cobble)-warmouth_sunfish 0.2443 0.2990 -0.3714 0.2579 0.8188 1.0232 1200
## scale(per_cobble)-white_sucker 0.3275 0.2696 -0.1769 0.3265 0.8786 1.0138 978
## scale(per_cobble)-yellow_bullhead_catfish 0.2454 0.2900 -0.3329 0.2454 0.8132 1.0115 1200
## scale(per_cobble)-sessile_animals 0.1688 0.2827 -0.4041 0.1605 0.7450 1.0101 1018
## scale(per_cobble)-aquatic_worms 0.2881 0.2606 -0.2093 0.2851 0.8444 1.0063 1200
## scale(per_cobble)-sow_bugs 0.2594 0.1902 -0.1028 0.2552 0.6481 1.0025 1200
## scale(per_cobble)-scuds 0.1169 0.1977 -0.2762 0.1167 0.4976 1.0020 1104
## scale(per_cobble)-water_mites 0.4795 0.2792 -0.0461 0.4722 1.0804 1.0004 1002
## scale(per_cobble)-damselfly_nymphs 0.3048 0.2302 -0.1535 0.3049 0.8031 1.0083 1322
## scale(per_cobble)-alderfly_larvae -0.1114 0.2526 -0.6186 -0.1017 0.3788 1.0106 1200
## scale(per_cobble)-other_beetles 0.1131 0.2426 -0.3766 0.1162 0.5751 1.0189 1200
## scale(per_cobble)-crayfish 0.4140 0.2280 -0.0100 0.4055 0.8887 1.0007 1200
## scale(per_cobble)-dragonfly_nymphs 0.1238 0.2552 -0.4338 0.1369 0.6048 1.0148 1200
## scale(per_cobble)-riffle_beetles 0.4058 0.2028 0.0218 0.3999 0.8379 1.0019 1061
## scale(per_cobble)-other_flies 0.2100 0.3031 -0.3640 0.2071 0.8107 0.9994 1200
## scale(per_cobble)-midges 0.2327 0.2894 -0.3331 0.2473 0.8020 1.0032 1791
## scale(per_cobble)-snails 0.0348 0.2600 -0.5080 0.0320 0.5303 1.0088 1030
## scale(per_cobble)-clams 0.0062 0.2689 -0.5431 0.0083 0.5179 1.0001 1064
## scale(per_cobble)-fishfly_larvae 0.3352 0.2207 -0.0943 0.3286 0.7635 1.0076 1200
## scale(per_cobble)-water_penny_beetles 0.4437 0.2230 0.0203 0.4407 0.8794 1.0006 1200
## scale(per_cobble)-cranefly_larvae 0.2391 0.2652 -0.2770 0.2361 0.7559 1.0038 1105
## scale(per_cobble)-ameletidae 0.2205 0.2739 -0.3339 0.2158 0.7868 1.0022 1529
## scale(per_cobble)-arthropleidae 0.2308 0.2950 -0.3884 0.2448 0.7853 1.0006 1200
## scale(per_cobble)-baetidae 0.3143 0.2531 -0.1722 0.3055 0.8202 1.0177 1200
## scale(per_cobble)-baetiscidae 0.2425 0.3109 -0.3665 0.2424 0.8998 1.0008 1200
## scale(per_cobble)-caenidae -0.2005 0.2628 -0.7624 -0.1883 0.2830 1.0001 1200
## scale(per_cobble)-ephemerellidae 0.2703 0.2817 -0.2961 0.2591 0.8270 0.9995 1200
## scale(per_cobble)-ephemeridae 0.2030 0.2931 -0.3968 0.2093 0.7869 1.0026 1200
## scale(per_cobble)-heptageniidae 0.2604 0.2014 -0.1290 0.2561 0.6796 1.0020 1200
## scale(per_cobble)-isonychiidae 0.2325 0.2964 -0.3742 0.2330 0.8224 1.0094 993
## scale(per_cobble)-leptohyphidae 0.0869 0.3095 -0.5382 0.0941 0.6954 1.0046 756
## scale(per_cobble)-leptophlebiidae 0.3437 0.1823 -0.0024 0.3431 0.6971 1.0006 1395
## scale(per_cobble)-polymitarcyidae 0.2328 0.3110 -0.4154 0.2351 0.8321 1.0092 1200
## scale(per_cobble)-potamanthidae 0.2441 0.3117 -0.3533 0.2341 0.9064 1.0055 1200
## scale(per_cobble)-pseudironidae 0.2359 0.3030 -0.3832 0.2373 0.8542 1.0015 1200
## scale(per_cobble)-siphlonuridae 0.2486 0.2946 -0.3144 0.2443 0.8350 1.0043 1200
## scale(per_cobble)-capniidae 0.2631 0.3085 -0.3054 0.2598 0.8957 1.0018 1200
## scale(per_cobble)-chloroperlidae 0.2849 0.2421 -0.1855 0.2837 0.7577 1.0061 1200
## scale(per_cobble)-leuctridae 0.4108 0.1820 0.0555 0.4154 0.7750 1.0002 1412
## scale(per_cobble)-nemouridae 0.3604 0.2203 -0.0659 0.3556 0.7974 1.0038 1084
## scale(per_cobble)-peltoperlidae 0.2430 0.3065 -0.3608 0.2416 0.8624 1.0013 1071
## scale(per_cobble)-perlidae 0.1059 0.1936 -0.2975 0.1117 0.4706 1.0033 1200
## scale(per_cobble)-perlodidae 0.2115 0.2921 -0.3658 0.2140 0.7979 1.0023 1200
## scale(per_cobble)-pteronarcyidae 0.2373 0.3086 -0.3763 0.2355 0.8325 1.0111 1101
## scale(per_cobble)-taeniopterygidae 0.2499 0.3048 -0.3598 0.2485 0.8408 1.0001 1200
## scale(per_cobble)-brachycentridae 0.2349 0.3175 -0.4371 0.2423 0.8978 1.0044 1200
## scale(per_cobble)-dipseuodopsidae 0.2385 0.3003 -0.3689 0.2396 0.8174 1.0038 1200
## scale(per_cobble)-glossosomatidae 0.3019 0.2783 -0.2534 0.3036 0.8513 1.0073 1200
## scale(per_cobble)-goeridae 0.2441 0.2986 -0.3808 0.2449 0.8378 1.0027 1200
## scale(per_cobble)-helicopsychidae 0.2395 0.2994 -0.3195 0.2334 0.8281 1.0026 1200
## scale(per_cobble)-hydropsychidae 0.2663 0.2092 -0.1489 0.2647 0.7038 1.0160 1200
## scale(per_cobble)-hydroptilidae 0.1683 0.2472 -0.3026 0.1712 0.6821 1.0018 1200
## scale(per_cobble)-lepidostomatidae 0.2161 0.2179 -0.2214 0.2211 0.6128 1.0032 1200
## scale(per_cobble)-leptoceridae 0.2442 0.3103 -0.4041 0.2524 0.8543 1.0004 1361
## scale(per_cobble)-limnephilidae 0.1752 0.2042 -0.2285 0.1797 0.5711 1.0031 1200
## scale(per_cobble)-molannidae 0.2071 0.2717 -0.3520 0.2126 0.7207 1.0094 1200
## scale(per_cobble)-odontoceridae 0.3321 0.2615 -0.1961 0.3236 0.8322 1.0074 1200
## scale(per_cobble)-philopotamidae 0.5303 0.2437 0.0946 0.5190 1.0736 1.0054 1101
## scale(per_cobble)-phryganeidae 0.0028 0.2927 -0.6084 0.0115 0.5469 1.0063 1014
## scale(per_cobble)-polycentropodidae 0.5448 0.2266 0.1332 0.5323 1.0323 1.0007 1200
## scale(per_cobble)-psychomiidae 0.2523 0.3092 -0.3375 0.2530 0.8308 1.0021 1200
## scale(per_cobble)-rhyacophilidae 0.4106 0.2243 -0.0025 0.3990 0.8768 1.0048 1200
## scale(per_cobble)-uenoidae 0.1198 0.2342 -0.3296 0.1184 0.5806 1.0048 1200
## scale(substrate_metric_total)-streambiotic_suff_mtdusky 0.4494 0.2298 0.0129 0.4366 0.9117 1.0054 1200
## scale(substrate_metric_total)-streambiotic_suff_nodusky 0.7050 0.2481 0.2531 0.6820 1.2354 1.0052 1200
## scale(substrate_metric_total)-streambiotic_suff_twolined 0.5463 0.2191 0.1407 0.5481 1.0151 1.0098 1200
## scale(substrate_metric_total)-streambiotic_suff_longtail 0.2580 0.2873 -0.3320 0.2695 0.8010 1.0002 1200
## scale(substrate_metric_total)-streambiotic_suff_red 0.3947 0.2507 -0.1000 0.3906 0.8853 1.0010 1200
## scale(substrate_metric_total)-streambiotic_suff_mole 0.1457 0.3033 -0.4961 0.1589 0.7081 1.0112 1048
## scale(substrate_metric_total)-streambiotic_suff_fourtoed 0.3598 0.3014 -0.2210 0.3640 0.9677 1.0054 1200
## scale(substrate_metric_total)-streambiotic_suff_redback 0.3076 0.2276 -0.1660 0.3072 0.7396 1.0025 1200
## scale(substrate_metric_total)-streambiotic_suff_slimy 0.4793 0.2643 -0.0198 0.4706 1.0111 1.0099 1200
## scale(substrate_metric_total)-blacknose_dace 0.6599 0.2818 0.1637 0.6520 1.2390 0.9997 1200
## scale(substrate_metric_total)-bluegill_sunfish 0.2775 0.3167 -0.3750 0.2837 0.8861 1.0030 1200
## scale(substrate_metric_total)-bluntnose_minnow 0.3383 0.3252 -0.3220 0.3414 0.9337 1.0073 1313
## scale(substrate_metric_total)-brindled_madtom 0.3486 0.3042 -0.2685 0.3526 0.9240 0.9996 1200
## scale(substrate_metric_total)-brook_stickleback 0.3468 0.3036 -0.2790 0.3580 0.9229 1.0025 1308
## scale(substrate_metric_total)-brook_trout 0.3328 0.3312 -0.3664 0.3253 0.9706 1.0059 1200
## scale(substrate_metric_total)-central_mudminnow 0.3583 0.3005 -0.2448 0.3482 0.9613 1.0090 1574
## scale(substrate_metric_total)-central_stoneroller_minnow 0.2931 0.3054 -0.3622 0.2900 0.8962 1.0079 1200
## scale(substrate_metric_total)-common_shiner 0.2943 0.3081 -0.3222 0.3020 0.9081 1.0252 1099
## scale(substrate_metric_total)-creek_chub 0.2988 0.2511 -0.2136 0.2979 0.7763 1.0032 1238
## scale(substrate_metric_total)-eastern_sand_darter 0.3626 0.3144 -0.2527 0.3621 0.9963 1.0025 1338
## scale(substrate_metric_total)-fantail_darter 0.3380 0.3071 -0.2775 0.3444 0.9264 0.9999 1200
## scale(substrate_metric_total)-fathead_minnow 0.3465 0.3096 -0.2413 0.3411 0.9901 1.0023 1200
## scale(substrate_metric_total)-golden_shiner 0.3458 0.3090 -0.2963 0.3615 0.9416 1.0084 1200
## scale(substrate_metric_total)-goldfish 0.3549 0.3181 -0.2877 0.3520 1.0113 1.0075 1200
## scale(substrate_metric_total)-grass_pickerel 0.3371 0.3129 -0.2932 0.3398 0.9891 1.0026 925
## scale(substrate_metric_total)-greenside_darter 0.3474 0.3028 -0.2561 0.3508 0.9455 1.0022 1200
## scale(substrate_metric_total)-green_sunfish 0.3731 0.3038 -0.1908 0.3660 0.9881 1.0037 1022
## scale(substrate_metric_total)-johnny_darter 0.2636 0.3070 -0.3434 0.2783 0.8555 1.0124 1200
## scale(substrate_metric_total)-largemouth_bass 0.3420 0.3070 -0.2399 0.3403 0.9182 1.0100 1200
## scale(substrate_metric_total)-longear_sunfish 0.3365 0.2996 -0.2711 0.3380 0.9449 1.0059 1200
## scale(substrate_metric_total)-longnose_dace 0.3462 0.3108 -0.2665 0.3468 0.9402 1.0076 1200
## scale(substrate_metric_total)-mottled_sculpin 0.3317 0.3108 -0.2992 0.3360 0.9292 1.0067 1365
## scale(substrate_metric_total)-northern_hog_sucker 0.3431 0.3090 -0.2739 0.3486 0.9401 1.0004 1200
## scale(substrate_metric_total)-pumpkinseed_sunfish 0.3391 0.3237 -0.3518 0.3502 0.9872 1.0067 1029
## scale(substrate_metric_total)-rainbow_darter 0.2930 0.2922 -0.2959 0.2976 0.8556 1.0171 1200
## scale(substrate_metric_total)-rainbow_trout 0.3578 0.3109 -0.2389 0.3456 0.9912 1.0009 1200
## scale(substrate_metric_total)-redbelly_dace 0.2798 0.3058 -0.3365 0.2772 0.8454 1.0013 1200
## scale(substrate_metric_total)-redside_dace 0.4056 0.2974 -0.1704 0.4026 1.0304 1.0008 1200
## scale(substrate_metric_total)-river_chub 0.3518 0.3069 -0.2568 0.3502 0.9510 1.0025 1104
## scale(substrate_metric_total)-silverjaw_minnow 0.3521 0.3159 -0.2681 0.3540 0.9932 1.0064 1200
## scale(substrate_metric_total)-smallmouth_bass 0.3473 0.3097 -0.2290 0.3456 0.9813 1.0055 1200
## scale(substrate_metric_total)-spotfin_shiner 0.3404 0.3183 -0.2865 0.3326 0.9620 0.9994 1098
## scale(substrate_metric_total)-stonecat_madtom 0.3511 0.3091 -0.2679 0.3462 0.9425 1.0009 1200
## scale(substrate_metric_total)-striped_shiner 0.3474 0.3107 -0.3041 0.3572 0.9811 1.0004 1303
## scale(substrate_metric_total)-trout_perch 0.3573 0.3160 -0.2967 0.3577 0.9755 1.0108 1200
## scale(substrate_metric_total)-warmouth_sunfish 0.3410 0.3202 -0.2953 0.3409 1.0134 1.0035 1499
## scale(substrate_metric_total)-white_sucker 0.2575 0.2941 -0.3218 0.2560 0.8383 1.0053 1200
## scale(substrate_metric_total)-yellow_bullhead_catfish 0.3175 0.3083 -0.3105 0.3257 0.8952 1.0159 1200
## scale(substrate_metric_total)-sessile_animals 0.5067 0.2892 -0.0368 0.4871 1.1131 1.0067 1200
## scale(substrate_metric_total)-aquatic_worms 0.3661 0.2749 -0.1712 0.3605 0.9462 1.0060 1200
## scale(substrate_metric_total)-sow_bugs 0.1399 0.2163 -0.3099 0.1419 0.5652 1.0003 1285
## scale(substrate_metric_total)-scuds 0.3148 0.2053 -0.0799 0.3122 0.7045 1.0048 1200
## scale(substrate_metric_total)-water_mites 0.6143 0.2694 0.1125 0.6117 1.1693 0.9995 1200
## scale(substrate_metric_total)-damselfly_nymphs 0.2162 0.2285 -0.2665 0.2177 0.6526 0.9995 1200
## scale(substrate_metric_total)-alderfly_larvae 0.3402 0.2499 -0.1661 0.3477 0.8347 1.0076 1200
## scale(substrate_metric_total)-other_beetles 0.0118 0.2631 -0.4874 0.0232 0.5160 1.0009 1200
## scale(substrate_metric_total)-crayfish 0.2709 0.2238 -0.1774 0.2741 0.7190 1.0193 1294
## scale(substrate_metric_total)-dragonfly_nymphs 0.2976 0.2421 -0.1906 0.2949 0.7450 1.0178 1200
## scale(substrate_metric_total)-riffle_beetles 0.2316 0.2018 -0.1656 0.2321 0.6095 1.0012 1200
## scale(substrate_metric_total)-other_flies 0.3677 0.2774 -0.1946 0.3702 0.9137 1.0008 1200
## scale(substrate_metric_total)-midges 0.3239 0.3049 -0.2888 0.3138 0.9076 1.0111 1200
## scale(substrate_metric_total)-snails 0.3499 0.2224 -0.0746 0.3418 0.7905 1.0116 1200
## scale(substrate_metric_total)-clams 0.1908 0.2533 -0.2899 0.1817 0.7169 1.0023 1185
## scale(substrate_metric_total)-fishfly_larvae 0.3544 0.2425 -0.1222 0.3491 0.8300 1.0039 1100
## scale(substrate_metric_total)-water_penny_beetles 0.7010 0.2681 0.2251 0.6847 1.2850 1.0013 1200
## scale(substrate_metric_total)-cranefly_larvae 0.4434 0.2588 -0.0515 0.4305 0.9842 1.0391 1200
## scale(substrate_metric_total)-ameletidae 0.3212 0.2689 -0.1942 0.3275 0.8144 1.0283 1200
## scale(substrate_metric_total)-arthropleidae 0.3456 0.3141 -0.2928 0.3373 0.9580 1.0002 1729
## scale(substrate_metric_total)-baetidae 0.4640 0.2296 0.0307 0.4556 0.9279 1.0055 1200
## scale(substrate_metric_total)-baetiscidae 0.3492 0.3157 -0.2575 0.3397 1.0392 1.0002 1091
## scale(substrate_metric_total)-caenidae 0.1573 0.2578 -0.3706 0.1647 0.6429 1.0124 1200
## scale(substrate_metric_total)-ephemerellidae 0.3075 0.2949 -0.3076 0.3232 0.8742 1.0014 1200
## scale(substrate_metric_total)-ephemeridae 0.3523 0.3050 -0.2534 0.3644 0.9293 0.9996 1200
## scale(substrate_metric_total)-heptageniidae 0.1549 0.2428 -0.3373 0.1609 0.6120 0.9989 1200
## scale(substrate_metric_total)-isonychiidae 0.3492 0.3141 -0.3120 0.3408 1.0081 1.0018 1200
## scale(substrate_metric_total)-leptohyphidae 0.2992 0.2923 -0.3186 0.2941 0.8511 1.0109 1027
## scale(substrate_metric_total)-leptophlebiidae 0.1838 0.2012 -0.2059 0.1804 0.6021 1.0042 976
## scale(substrate_metric_total)-polymitarcyidae 0.3534 0.3118 -0.2871 0.3554 0.9771 1.0024 1200
## scale(substrate_metric_total)-potamanthidae 0.3489 0.3139 -0.2827 0.3440 0.9704 1.0161 1200
## scale(substrate_metric_total)-pseudironidae 0.3584 0.3151 -0.2649 0.3446 0.9925 1.0003 1200
## scale(substrate_metric_total)-siphlonuridae 0.3419 0.3129 -0.2980 0.3517 1.0101 1.0014 1200
## scale(substrate_metric_total)-capniidae 0.3492 0.3120 -0.2571 0.3528 0.9736 1.0093 1200
## scale(substrate_metric_total)-chloroperlidae 0.3372 0.2502 -0.1388 0.3283 0.8305 1.0103 1200
## scale(substrate_metric_total)-leuctridae 0.3978 0.2036 -0.0091 0.3974 0.7847 0.9993 1200
## scale(substrate_metric_total)-nemouridae 0.3457 0.2416 -0.1345 0.3516 0.8026 1.0005 1200
## scale(substrate_metric_total)-peltoperlidae 0.3530 0.3127 -0.2623 0.3557 0.9904 1.0051 1200
## scale(substrate_metric_total)-perlidae 0.0733 0.2266 -0.3832 0.0757 0.4898 1.0020 1200
## scale(substrate_metric_total)-perlodidae 0.3253 0.3029 -0.3155 0.3227 0.8988 1.0110 1200
## scale(substrate_metric_total)-pteronarcyidae 0.3402 0.3224 -0.3022 0.3417 0.9944 1.0239 1200
## scale(substrate_metric_total)-taeniopterygidae 0.3540 0.3098 -0.2746 0.3565 0.9461 1.0005 1200
## scale(substrate_metric_total)-brachycentridae 0.3407 0.3140 -0.2546 0.3385 0.9818 1.0225 1200
## scale(substrate_metric_total)-dipseuodopsidae 0.3433 0.2993 -0.2446 0.3428 0.9390 1.0013 1030
## scale(substrate_metric_total)-glossosomatidae 0.3316 0.2914 -0.2992 0.3308 0.8878 0.9994 1200
## scale(substrate_metric_total)-goeridae 0.3430 0.3114 -0.2942 0.3507 0.9579 1.0101 1200
## scale(substrate_metric_total)-helicopsychidae 0.3524 0.3015 -0.2559 0.3523 0.9320 1.0034 1200
## scale(substrate_metric_total)-hydropsychidae 0.6472 0.2276 0.2268 0.6325 1.0976 1.0037 1200
## scale(substrate_metric_total)-hydroptilidae 0.5885 0.2677 0.1244 0.5717 1.1474 1.0207 1200
## scale(substrate_metric_total)-lepidostomatidae 0.2080 0.2292 -0.2474 0.2139 0.6379 1.0155 1200
## scale(substrate_metric_total)-leptoceridae 0.3487 0.3174 -0.2802 0.3562 0.9661 1.0020 1200
## scale(substrate_metric_total)-limnephilidae 0.3009 0.2272 -0.1327 0.2982 0.7497 0.9990 1200
## scale(substrate_metric_total)-molannidae 0.3407 0.2888 -0.2423 0.3365 0.9301 1.0044 1200
## scale(substrate_metric_total)-odontoceridae 0.3163 0.2862 -0.2372 0.3139 0.8986 1.0010 1200
## scale(substrate_metric_total)-philopotamidae 0.6389 0.2566 0.1821 0.6255 1.1879 1.0041 1200
## scale(substrate_metric_total)-phryganeidae 0.2474 0.2968 -0.3541 0.2660 0.8129 1.0058 1200
## scale(substrate_metric_total)-polycentropodidae 0.3778 0.2287 -0.0352 0.3700 0.8524 1.0093 1200
## scale(substrate_metric_total)-psychomiidae 0.3410 0.3106 -0.2603 0.3365 0.9603 1.0017 1039
## scale(substrate_metric_total)-rhyacophilidae 0.5598 0.2534 0.0905 0.5553 1.0876 1.0124 1200
## scale(substrate_metric_total)-uenoidae 0.4026 0.2517 -0.1034 0.3973 0.8989 1.0273 1200
## scale(pool_metric_total)-streambiotic_suff_mtdusky 0.2485 0.2124 -0.1457 0.2433 0.6714 1.0003 1200
## scale(pool_metric_total)-streambiotic_suff_nodusky -0.0817 0.1909 -0.4610 -0.0796 0.2856 1.0081 1200
## scale(pool_metric_total)-streambiotic_suff_twolined 0.2682 0.1895 -0.0799 0.2649 0.6671 1.0008 1200
## scale(pool_metric_total)-streambiotic_suff_longtail -0.0455 0.2843 -0.6355 -0.0330 0.4811 1.0038 1200
## scale(pool_metric_total)-streambiotic_suff_red -0.5066 0.2511 -1.0515 -0.4938 -0.0594 1.0025 1087
## scale(pool_metric_total)-streambiotic_suff_mole -0.1470 0.3096 -0.8091 -0.1418 0.4219 1.0042 1200
## scale(pool_metric_total)-streambiotic_suff_fourtoed -0.0012 0.3249 -0.6194 0.0012 0.6388 1.0085 1200
## scale(pool_metric_total)-streambiotic_suff_redback -0.4147 0.2438 -0.9194 -0.4076 0.0691 0.9989 1200
## scale(pool_metric_total)-streambiotic_suff_slimy -0.1293 0.2493 -0.5990 -0.1279 0.3531 1.0041 1200
## scale(pool_metric_total)-blacknose_dace 0.1040 0.2629 -0.3735 0.0921 0.6760 1.0052 1200
## scale(pool_metric_total)-bluegill_sunfish 0.0055 0.3075 -0.5871 0.0061 0.6292 1.0008 1200
## scale(pool_metric_total)-bluntnose_minnow -0.0037 0.3188 -0.6373 0.0044 0.6693 1.0038 1200
## scale(pool_metric_total)-brindled_madtom -0.0112 0.3210 -0.6640 0.0010 0.6055 1.0209 1392
## scale(pool_metric_total)-brook_stickleback -0.0149 0.3212 -0.6242 -0.0220 0.6157 1.0001 1037
## scale(pool_metric_total)-brook_trout -0.0275 0.3038 -0.6481 -0.0120 0.5531 1.0312 1200
## scale(pool_metric_total)-central_mudminnow -0.0152 0.3156 -0.6537 -0.0054 0.6013 1.0037 1327
## scale(pool_metric_total)-central_stoneroller_minnow 0.0108 0.3207 -0.6469 0.0144 0.6674 1.0056 1200
## scale(pool_metric_total)-common_shiner -0.0248 0.3278 -0.6875 -0.0133 0.6073 1.0058 1026
## scale(pool_metric_total)-creek_chub 0.1583 0.2584 -0.3372 0.1458 0.7010 1.0112 1247
## scale(pool_metric_total)-eastern_sand_darter 0.0003 0.3239 -0.6604 0.0105 0.6516 1.0004 1200
## scale(pool_metric_total)-fantail_darter -0.0111 0.3136 -0.6041 -0.0012 0.6081 1.0136 1081
## scale(pool_metric_total)-fathead_minnow -0.0128 0.3150 -0.6457 -0.0125 0.6364 1.0073 1200
## scale(pool_metric_total)-golden_shiner -0.0265 0.3078 -0.6155 -0.0353 0.5800 1.0002 1200
## scale(pool_metric_total)-goldfish -0.0048 0.3187 -0.6140 -0.0216 0.6492 1.0065 1200
## scale(pool_metric_total)-grass_pickerel -0.0071 0.3116 -0.6190 -0.0105 0.6440 1.0040 1200
## scale(pool_metric_total)-greenside_darter -0.0071 0.3307 -0.6773 -0.0082 0.6423 1.0029 1200
## scale(pool_metric_total)-green_sunfish 0.0302 0.3144 -0.5781 0.0347 0.6676 1.0004 1304
## scale(pool_metric_total)-johnny_darter -0.0904 0.3054 -0.7062 -0.0904 0.4804 1.0069 1200
## scale(pool_metric_total)-largemouth_bass -0.0098 0.3238 -0.6992 -0.0144 0.6026 1.0033 1200
## scale(pool_metric_total)-longear_sunfish -0.0178 0.3141 -0.6484 -0.0170 0.5922 1.0083 1200
## scale(pool_metric_total)-longnose_dace -0.0244 0.3187 -0.6554 -0.0278 0.6189 1.0054 1200
## scale(pool_metric_total)-mottled_sculpin -0.0224 0.3047 -0.5996 -0.0317 0.6125 1.0024 1200
## scale(pool_metric_total)-northern_hog_sucker -0.0141 0.3298 -0.6320 -0.0196 0.6661 1.0095 1066
## scale(pool_metric_total)-pumpkinseed_sunfish -0.0176 0.3163 -0.6883 -0.0066 0.6099 1.0026 1164
## scale(pool_metric_total)-rainbow_darter -0.1066 0.2958 -0.7434 -0.0955 0.4603 1.0006 1200
## scale(pool_metric_total)-rainbow_trout -0.0049 0.3216 -0.6252 -0.0005 0.6331 1.0080 1200
## scale(pool_metric_total)-redbelly_dace -0.0339 0.3199 -0.7037 -0.0272 0.5710 1.0008 1200
## scale(pool_metric_total)-redside_dace 0.0124 0.3109 -0.5890 0.0128 0.6411 1.0062 1011
## scale(pool_metric_total)-river_chub 0.0046 0.3273 -0.6588 -0.0064 0.6722 1.0019 1200
## scale(pool_metric_total)-silverjaw_minnow 0.0016 0.3090 -0.6242 -0.0039 0.6188 1.0142 1200
## scale(pool_metric_total)-smallmouth_bass -0.0230 0.3278 -0.6588 -0.0242 0.6340 0.9992 1084
## scale(pool_metric_total)-spotfin_shiner 0.0003 0.3226 -0.6463 0.0034 0.6321 1.0127 1440
## scale(pool_metric_total)-stonecat_madtom -0.0191 0.3127 -0.6433 -0.0054 0.5992 1.0061 1200
## scale(pool_metric_total)-striped_shiner -0.0011 0.2986 -0.6305 -0.0057 0.5946 1.0008 1200
## scale(pool_metric_total)-trout_perch 0.0048 0.3181 -0.6287 -0.0029 0.6363 1.0011 1331
## scale(pool_metric_total)-warmouth_sunfish -0.0106 0.3226 -0.6442 -0.0146 0.6095 1.0018 1200
## scale(pool_metric_total)-white_sucker -0.1158 0.3090 -0.7454 -0.1145 0.4673 1.0125 1200
## scale(pool_metric_total)-yellow_bullhead_catfish 0.0079 0.2981 -0.5610 0.0124 0.6076 1.0187 1200
## scale(pool_metric_total)-sessile_animals 0.0603 0.3054 -0.4856 0.0450 0.6666 1.0030 1200
## scale(pool_metric_total)-aquatic_worms -0.3389 0.2756 -0.9258 -0.3208 0.1778 1.0190 1200
## scale(pool_metric_total)-sow_bugs 0.0167 0.2048 -0.3861 0.0047 0.4193 1.0042 1310
## scale(pool_metric_total)-scuds -0.3650 0.1996 -0.7797 -0.3568 0.0048 1.0013 1098
## scale(pool_metric_total)-water_mites 0.1875 0.2541 -0.2929 0.1824 0.6937 1.0046 1274
## scale(pool_metric_total)-damselfly_nymphs 0.1646 0.2298 -0.2686 0.1532 0.6287 1.0032 1200
## scale(pool_metric_total)-alderfly_larvae 0.2169 0.2340 -0.2728 0.2243 0.6625 1.0065 1200
## scale(pool_metric_total)-other_beetles -0.0137 0.2608 -0.5352 -0.0156 0.5057 1.0006 1200
## scale(pool_metric_total)-crayfish 0.1031 0.2298 -0.3733 0.1032 0.5549 1.0085 1200
## scale(pool_metric_total)-dragonfly_nymphs -0.1952 0.2361 -0.6907 -0.1806 0.2425 1.0023 1200
## scale(pool_metric_total)-riffle_beetles 0.0835 0.1868 -0.2722 0.0853 0.4435 1.0185 1200
## scale(pool_metric_total)-other_flies 0.1752 0.2944 -0.3654 0.1667 0.7710 1.0017 1200
## scale(pool_metric_total)-midges -0.0289 0.3137 -0.6403 -0.0265 0.5447 1.0023 1200
## scale(pool_metric_total)-snails 0.2252 0.2292 -0.1993 0.2202 0.7228 1.0053 1200
## scale(pool_metric_total)-clams -0.0208 0.2666 -0.5694 -0.0065 0.4457 1.0011 1200
## scale(pool_metric_total)-fishfly_larvae -0.0295 0.2242 -0.4668 -0.0226 0.4098 1.0061 1200
## scale(pool_metric_total)-water_penny_beetles 0.0830 0.2086 -0.3040 0.0804 0.4905 1.0091 1200
## scale(pool_metric_total)-cranefly_larvae 0.2186 0.2600 -0.2919 0.2133 0.7333 1.0019 1200
## scale(pool_metric_total)-ameletidae 0.2320 0.2924 -0.3240 0.2191 0.8350 0.9995 1200
## scale(pool_metric_total)-arthropleidae -0.0159 0.3305 -0.6552 -0.0188 0.6579 1.0108 918
## scale(pool_metric_total)-baetidae 0.1930 0.2292 -0.2432 0.1914 0.6470 1.0136 1200
## scale(pool_metric_total)-baetiscidae -0.0047 0.3054 -0.6233 -0.0071 0.5846 1.0011 1085
## scale(pool_metric_total)-caenidae 0.1408 0.2638 -0.3401 0.1317 0.6788 1.0213 1200
## scale(pool_metric_total)-ephemerellidae 0.1058 0.3139 -0.5099 0.0908 0.7465 1.0072 806
## scale(pool_metric_total)-ephemeridae -0.0133 0.3014 -0.5670 -0.0170 0.5933 1.0020 1200
## scale(pool_metric_total)-heptageniidae -0.0218 0.2178 -0.4274 -0.0217 0.4231 1.0075 1054
## scale(pool_metric_total)-isonychiidae -0.0244 0.3164 -0.6559 -0.0227 0.6161 1.0142 1200
## scale(pool_metric_total)-leptohyphidae 0.1318 0.3099 -0.5004 0.1244 0.7414 1.0018 1200
## scale(pool_metric_total)-leptophlebiidae -0.1210 0.1768 -0.4688 -0.1200 0.2157 1.0117 1200
## scale(pool_metric_total)-polymitarcyidae -0.0215 0.3129 -0.6436 -0.0205 0.6129 1.0020 1467
## scale(pool_metric_total)-potamanthidae -0.0035 0.3157 -0.6166 -0.0101 0.6041 1.0029 1200
## scale(pool_metric_total)-pseudironidae -0.0133 0.3231 -0.6589 -0.0129 0.6034 1.0015 1200
## scale(pool_metric_total)-siphlonuridae -0.0161 0.3079 -0.5946 -0.0269 0.6423 1.0100 1200
## scale(pool_metric_total)-capniidae 0.0000 0.3119 -0.6008 0.0008 0.6320 1.0170 1200
## scale(pool_metric_total)-chloroperlidae 0.0760 0.2545 -0.4291 0.0826 0.5872 1.0045 1200
## scale(pool_metric_total)-leuctridae -0.1160 0.1751 -0.4640 -0.1150 0.2074 1.0023 1200
## scale(pool_metric_total)-nemouridae -0.0831 0.2286 -0.5295 -0.0832 0.3665 1.0218 1200
## scale(pool_metric_total)-peltoperlidae -0.0056 0.3035 -0.6336 -0.0144 0.5869 1.0055 1026
## scale(pool_metric_total)-perlidae 0.3449 0.2133 -0.0314 0.3308 0.8007 1.0012 1096
## scale(pool_metric_total)-perlodidae -0.0179 0.2966 -0.6046 -0.0085 0.5373 1.0159 1200
## scale(pool_metric_total)-pteronarcyidae -0.0050 0.3167 -0.6255 -0.0099 0.6041 1.0017 1211
## scale(pool_metric_total)-taeniopterygidae -0.0083 0.3187 -0.6570 -0.0162 0.6717 1.0046 1232
## scale(pool_metric_total)-brachycentridae -0.0007 0.3072 -0.6120 -0.0002 0.6167 1.0068 1242
## scale(pool_metric_total)-dipseuodopsidae -0.0040 0.3253 -0.6508 -0.0072 0.6202 1.0084 1200
## scale(pool_metric_total)-glossosomatidae -0.0558 0.2743 -0.6064 -0.0606 0.4958 1.0222 1101
## scale(pool_metric_total)-goeridae -0.0080 0.3191 -0.6168 -0.0128 0.6533 1.0014 1200
## scale(pool_metric_total)-helicopsychidae -0.0056 0.3183 -0.6151 -0.0146 0.6430 1.0002 1306
## scale(pool_metric_total)-hydropsychidae -0.0245 0.1930 -0.4014 -0.0245 0.3638 1.0151 1200
## scale(pool_metric_total)-hydroptilidae -0.0445 0.2591 -0.5655 -0.0483 0.4875 1.0174 1200
## scale(pool_metric_total)-lepidostomatidae -0.1171 0.1943 -0.5186 -0.1071 0.2343 1.0004 1220
## scale(pool_metric_total)-leptoceridae -0.0206 0.3292 -0.6601 -0.0248 0.6271 1.0029 1200
## scale(pool_metric_total)-limnephilidae -0.2047 0.2108 -0.6398 -0.1900 0.1788 1.0003 1200
## scale(pool_metric_total)-molannidae -0.1774 0.2787 -0.7613 -0.1676 0.3659 1.0053 1200
## scale(pool_metric_total)-odontoceridae -0.3154 0.2879 -0.9252 -0.3057 0.2102 1.0084 1200
## scale(pool_metric_total)-philopotamidae -0.2221 0.2255 -0.7130 -0.2044 0.1962 1.0006 1200
## scale(pool_metric_total)-phryganeidae -0.1644 0.2733 -0.7329 -0.1542 0.3642 1.0034 1200
## scale(pool_metric_total)-polycentropodidae -0.0530 0.1933 -0.4746 -0.0516 0.2994 1.0294 1200
## scale(pool_metric_total)-psychomiidae -0.0145 0.3120 -0.6120 -0.0111 0.5716 1.0052 1200
## scale(pool_metric_total)-rhyacophilidae -0.1035 0.2247 -0.5480 -0.0913 0.3480 1.0048 1200
## scale(pool_metric_total)-uenoidae 0.0628 0.2272 -0.3937 0.0631 0.5086 1.0044 1200
##
## Detection (logit scale):
## Mean SD 2.5% 50% 97.5% Rhat ESS
## (Intercept)-streambiotic_suff_mtdusky -0.2914 0.3078 -0.8823 -0.3007 0.3236 1.0100 1200
## (Intercept)-streambiotic_suff_nodusky -0.2558 0.1790 -0.6103 -0.2578 0.0880 1.0104 1200
## (Intercept)-streambiotic_suff_twolined 1.4451 0.1416 1.1720 1.4412 1.7236 1.0215 1200
## (Intercept)-streambiotic_suff_longtail -1.8625 0.8383 -3.6466 -1.7982 -0.3676 1.0052 772
## (Intercept)-streambiotic_suff_red -0.8582 0.3406 -1.5083 -0.8641 -0.1866 1.0020 1200
## (Intercept)-streambiotic_suff_mole -2.9449 1.4637 -5.9136 -2.8580 -0.4228 1.0349 114
## (Intercept)-streambiotic_suff_fourtoed -2.4440 2.5086 -8.3143 -2.1281 1.6399 1.1088 38
## (Intercept)-streambiotic_suff_redback -0.4285 0.1641 -0.7467 -0.4250 -0.1194 1.0035 1200
## (Intercept)-streambiotic_suff_slimy -1.6962 0.6748 -3.0118 -1.6839 -0.4629 0.9996 1200
## (Intercept)-blacknose_dace -1.9011 0.4428 -2.8552 -1.8904 -1.0864 1.0020 1200
## (Intercept)-bluegill_sunfish -2.6766 1.8041 -6.4054 -2.3838 0.2415 1.1059 59
## (Intercept)-bluntnose_minnow -2.4916 2.6427 -8.6599 -2.1462 1.9154 1.2410 41
## (Intercept)-brindled_madtom -2.3606 2.4936 -8.2588 -2.1136 1.8225 1.0778 76
## (Intercept)-brook_stickleback -2.4250 2.5262 -8.2330 -2.2046 1.8824 1.1898 73
## (Intercept)-brook_trout -2.4368 2.6132 -8.1440 -2.1353 1.6340 1.1910 73
## (Intercept)-central_mudminnow -2.1988 2.5748 -8.1423 -1.9547 2.2522 1.1559 44
## (Intercept)-central_stoneroller_minnow -4.1764 1.0953 -6.0463 -4.3117 -1.7766 1.0478 168
## (Intercept)-common_shiner -2.9831 1.6973 -6.5375 -2.8389 0.0159 1.0960 75
## (Intercept)-creek_chub -1.8477 0.2616 -2.3363 -1.8533 -1.3231 1.0165 1200
## (Intercept)-eastern_sand_darter -2.4486 2.4737 -8.0662 -2.3141 2.0632 1.1641 80
## (Intercept)-fantail_darter -2.4621 2.5101 -7.8373 -2.2349 1.7915 1.0989 78
## (Intercept)-fathead_minnow -2.4723 2.6290 -8.5944 -2.0776 1.9317 1.1516 36
## (Intercept)-golden_shiner -2.3984 2.5076 -8.2144 -2.1050 1.8264 1.0975 68
## (Intercept)-goldfish -2.5517 2.4666 -8.2571 -2.2695 1.6762 1.1073 68
## (Intercept)-grass_pickerel -2.3993 2.5378 -8.1277 -2.1712 1.9566 1.1506 68
## (Intercept)-greenside_darter -2.3624 2.5468 -7.9913 -2.1201 2.0741 1.1043 88
## (Intercept)-green_sunfish -4.1827 1.3169 -6.5877 -4.2588 -1.5651 1.0078 165
## (Intercept)-johnny_darter -3.2856 1.1297 -5.3953 -3.3127 -1.0551 1.0180 183
## (Intercept)-largemouth_bass -2.3234 2.4294 -7.9822 -2.0658 1.8450 1.0934 71
## (Intercept)-longear_sunfish -2.3466 2.4095 -7.6200 -2.1262 2.1507 1.1010 62
## (Intercept)-longnose_dace -2.5289 2.4990 -8.1012 -2.2800 1.9572 1.1219 70
## (Intercept)-mottled_sculpin -2.3371 2.5359 -8.1357 -2.0348 2.0491 1.1076 85
## (Intercept)-northern_hog_sucker -2.5142 2.4823 -8.0926 -2.3154 1.7104 1.0997 112
## (Intercept)-pumpkinseed_sunfish -2.3195 2.4301 -7.5600 -2.0708 1.9404 1.0819 83
## (Intercept)-rainbow_darter -2.0014 0.9941 -4.1952 -1.8907 -0.2793 1.0100 319
## (Intercept)-rainbow_trout -2.3874 2.5284 -8.1079 -2.1566 1.8102 1.1034 44
## (Intercept)-redbelly_dace -3.4341 1.4092 -6.1523 -3.3884 -0.9159 1.0426 254
## (Intercept)-redside_dace -3.2591 1.6892 -6.7621 -3.0568 -0.4367 1.0848 66
## (Intercept)-river_chub -2.4837 2.4681 -8.1890 -2.3056 1.5278 1.1575 47
## (Intercept)-silverjaw_minnow -2.3307 2.3479 -8.0135 -2.1353 1.8411 1.1308 57
## (Intercept)-smallmouth_bass -2.3410 2.4102 -7.8480 -2.1545 1.8437 1.0926 45
## (Intercept)-spotfin_shiner -2.3585 2.4561 -8.1076 -2.1394 1.9856 1.1190 69
## (Intercept)-stonecat_madtom -2.4520 2.5752 -8.4653 -2.2294 1.9326 1.2455 82
## (Intercept)-striped_shiner -2.3044 2.3965 -7.6039 -2.0895 1.9722 1.1216 76
## (Intercept)-trout_perch -2.2398 2.4065 -7.5753 -1.9360 1.8791 1.0707 61
## (Intercept)-warmouth_sunfish -2.4565 2.5343 -8.2805 -2.2178 1.6799 1.0936 43
## (Intercept)-white_sucker -2.9844 1.2165 -5.2154 -3.0055 -0.7442 1.0612 169
## (Intercept)-yellow_bullhead_catfish -2.8236 1.9484 -6.7769 -2.6077 0.4685 1.0604 102
## (Intercept)-sessile_animals -2.9783 1.2888 -5.2886 -3.0022 -0.5222 1.0012 288
## (Intercept)-aquatic_worms 0.6181 0.1108 0.4097 0.6180 0.8431 0.9994 1200
## (Intercept)-sow_bugs 0.0351 0.1962 -0.3493 0.0355 0.4155 0.9995 1200
## (Intercept)-scuds -0.2698 0.1817 -0.6343 -0.2687 0.0867 1.0102 1200
## (Intercept)-water_mites -1.2911 0.1975 -1.6772 -1.2924 -0.9024 1.0033 1200
## (Intercept)-damselfly_nymphs -0.2754 0.1382 -0.5615 -0.2697 0.0038 1.0106 1200
## (Intercept)-alderfly_larvae -1.0421 0.1891 -1.3984 -1.0401 -0.6844 1.0021 1307
## (Intercept)-other_beetles -0.0246 0.1201 -0.2547 -0.0261 0.2204 1.0067 1200
## (Intercept)-crayfish 0.6413 0.1214 0.4018 0.6441 0.8811 1.0041 1200
## (Intercept)-dragonfly_nymphs -0.1140 0.1236 -0.3584 -0.1156 0.1256 1.0067 1200
## (Intercept)-riffle_beetles 0.0677 0.1370 -0.1854 0.0698 0.3358 1.0135 1453
## (Intercept)-other_flies 1.6953 0.1330 1.4487 1.6916 1.9651 1.0147 1200
## (Intercept)-midges 1.7920 0.1379 1.5161 1.7889 2.0679 1.0039 1200
## (Intercept)-snails -0.0316 0.1190 -0.2517 -0.0299 0.1994 1.0041 1200
## (Intercept)-clams -1.3937 0.2908 -1.9730 -1.3854 -0.8416 1.0119 776
## (Intercept)-fishfly_larvae -0.7965 0.1914 -1.1995 -0.7970 -0.4230 1.0067 1200
## (Intercept)-water_penny_beetles -0.9121 0.2179 -1.3530 -0.9169 -0.4644 1.0317 1200
## (Intercept)-cranefly_larvae 0.9855 0.1174 0.7708 0.9810 1.2181 1.0029 1200
## (Intercept)-ameletidae -2.4044 0.2623 -2.9779 -2.3926 -1.9226 0.9997 1085
## (Intercept)-arthropleidae -2.4860 2.5392 -8.2959 -2.2300 1.6794 1.0810 59
## (Intercept)-baetidae 0.4092 0.1311 0.1476 0.4086 0.6593 1.0136 1200
## (Intercept)-baetiscidae -2.4159 2.4326 -7.8271 -2.1853 1.8803 1.1091 92
## (Intercept)-caenidae -1.0183 0.5029 -2.1489 -0.9827 -0.1586 1.0050 1200
## (Intercept)-ephemerellidae -4.0193 1.0853 -5.7958 -4.2026 -1.7746 1.0530 169
## (Intercept)-ephemeridae -3.3444 1.3751 -5.7248 -3.3904 -0.6845 1.1421 116
## (Intercept)-heptageniidae -0.6951 0.2260 -1.1541 -0.6875 -0.2616 1.0006 1200
## (Intercept)-isonychiidae -2.4865 2.4374 -7.9426 -2.1930 1.6888 1.1219 39
## (Intercept)-leptohyphidae -3.1989 0.8090 -4.5762 -3.2970 -1.5032 1.0190 262
## (Intercept)-leptophlebiidae 0.5817 0.1684 0.2675 0.5793 0.9155 1.0017 1200
## (Intercept)-polymitarcyidae -2.4090 2.4607 -8.0205 -2.2101 1.7337 1.0833 51
## (Intercept)-potamanthidae -2.5168 2.5528 -8.3855 -2.2856 1.7607 1.1255 74
## (Intercept)-pseudironidae -2.4157 2.5236 -7.7872 -2.1981 1.6838 1.1253 87
## (Intercept)-siphlonuridae -2.2606 2.4535 -7.4841 -2.0885 1.9264 1.0558 56
## (Intercept)-capniidae -2.3979 2.4413 -7.6042 -2.1606 1.6089 1.1048 74
## (Intercept)-chloroperlidae -1.5697 0.4259 -2.4468 -1.5524 -0.7894 1.0234 848
## (Intercept)-leuctridae 0.7916 0.1564 0.4920 0.7918 1.1001 0.9998 1200
## (Intercept)-nemouridae -1.2491 0.2449 -1.7594 -1.2422 -0.8109 1.0111 1315
## (Intercept)-peltoperlidae -2.5557 2.5538 -8.0919 -2.3327 1.7069 1.1291 66
## (Intercept)-perlidae -0.2311 0.1894 -0.5944 -0.2259 0.1214 1.0019 1165
## (Intercept)-perlodidae -3.2312 0.3845 -3.9276 -3.2507 -2.4047 1.0147 656
## (Intercept)-pteronarcyidae -2.2416 2.3968 -7.7128 -2.0586 2.0355 1.0890 42
## (Intercept)-taeniopterygidae -2.3336 2.5536 -8.4495 -2.1545 1.9275 1.1497 69
## (Intercept)-brachycentridae -2.3151 2.3445 -7.5422 -2.1183 1.7410 1.1496 74
## (Intercept)-dipseuodopsidae -2.4638 2.5645 -8.4706 -2.2159 2.0027 1.1222 107
## (Intercept)-glossosomatidae -0.6958 0.7425 -2.2693 -0.6708 0.6758 1.0011 1544
## (Intercept)-goeridae -2.2967 2.3659 -7.6550 -2.2005 1.6332 1.0908 55
## (Intercept)-helicopsychidae -2.4151 2.4361 -7.9637 -2.2285 1.7999 1.1111 36
## (Intercept)-hydropsychidae 0.9845 0.1235 0.7411 0.9886 1.2173 1.0011 1478
## (Intercept)-hydroptilidae -1.7222 0.4446 -2.6088 -1.7124 -0.8926 0.9994 1058
## (Intercept)-lepidostomatidae -0.0396 0.2138 -0.4597 -0.0334 0.3630 1.0039 1022
## (Intercept)-leptoceridae -2.3503 2.4481 -8.1601 -2.1322 1.6888 1.1810 59
## (Intercept)-limnephilidae -0.4565 0.1964 -0.8381 -0.4581 -0.0904 1.0022 1200
## (Intercept)-molannidae -1.2748 0.9122 -3.1035 -1.2469 0.4831 1.0071 989
## (Intercept)-odontoceridae -2.0221 0.9209 -3.9958 -1.9658 -0.3853 1.0008 877
## (Intercept)-philopotamidae -0.7041 0.1650 -1.0292 -0.7095 -0.3739 1.0025 1200
## (Intercept)-phryganeidae -2.6549 0.8377 -4.3007 -2.6346 -1.1960 1.0630 390
## (Intercept)-polycentropodidae -0.4526 0.1794 -0.7968 -0.4538 -0.0988 0.9999 1200
## (Intercept)-psychomiidae -2.3476 2.4979 -8.0579 -2.0057 1.7895 1.1135 58
## (Intercept)-rhyacophilidae -1.1480 0.3097 -1.7541 -1.1455 -0.5633 1.0043 1200
## (Intercept)-uenoidae -1.2592 0.2428 -1.7636 -1.2601 -0.7993 0.9996 1200
## scale(seasons)-streambiotic_suff_mtdusky -0.9483 0.2300 -1.4407 -0.9381 -0.5424 1.0059 1200
## scale(seasons)-streambiotic_suff_nodusky -0.4391 0.1477 -0.7468 -0.4372 -0.1691 1.0070 1200
## scale(seasons)-streambiotic_suff_twolined -0.2331 0.1373 -0.5065 -0.2320 0.0263 1.0046 1200
## scale(seasons)-streambiotic_suff_longtail -0.3800 0.3842 -1.1835 -0.3779 0.3540 1.0016 1200
## scale(seasons)-streambiotic_suff_red -0.7282 0.2958 -1.3331 -0.7254 -0.1434 1.0087 1200
## scale(seasons)-streambiotic_suff_mole -0.0778 0.4543 -0.9638 -0.0739 0.8260 1.0003 1200
## scale(seasons)-streambiotic_suff_fourtoed -0.2313 0.5608 -1.3591 -0.2188 0.8668 1.0045 1200
## scale(seasons)-streambiotic_suff_redback -0.5561 0.1389 -0.8452 -0.5523 -0.2877 1.0015 1200
## scale(seasons)-streambiotic_suff_slimy -0.7388 0.4130 -1.5970 -0.7236 -0.0033 1.0002 1318
## scale(seasons)-blacknose_dace -1.0679 0.2509 -1.5697 -1.0594 -0.5939 1.0015 1200
## scale(seasons)-bluegill_sunfish 0.0699 0.5126 -0.9251 0.0633 1.1312 1.0121 1200
## scale(seasons)-bluntnose_minnow -0.1938 0.5764 -1.3177 -0.1841 0.9488 1.0024 1296
## scale(seasons)-brindled_madtom -0.2307 0.5616 -1.3320 -0.1989 0.7906 1.0010 1572
## scale(seasons)-brook_stickleback -0.2055 0.5802 -1.3157 -0.2045 0.9245 1.0098 1200
## scale(seasons)-brook_trout -0.2024 0.5584 -1.2795 -0.2102 0.8934 1.0061 1200
## scale(seasons)-central_mudminnow -0.2184 0.5610 -1.3450 -0.2052 0.8461 1.0015 1200
## scale(seasons)-central_stoneroller_minnow -0.1696 0.4167 -0.9898 -0.1777 0.6639 1.0011 1200
## scale(seasons)-common_shiner -0.4334 0.4935 -1.4196 -0.4258 0.5259 1.0062 1308
## scale(seasons)-creek_chub -1.0593 0.2066 -1.4779 -1.0607 -0.6626 1.0005 1200
## scale(seasons)-eastern_sand_darter -0.1979 0.5650 -1.3591 -0.1885 0.8976 1.0002 1093
## scale(seasons)-fantail_darter -0.2186 0.5767 -1.3935 -0.2003 0.8707 1.0105 1200
## scale(seasons)-fathead_minnow -0.1851 0.5624 -1.2654 -0.1845 0.9097 1.0166 731
## scale(seasons)-golden_shiner -0.1866 0.5523 -1.2735 -0.1642 0.8850 1.0016 1200
## scale(seasons)-goldfish -0.2090 0.5747 -1.3509 -0.1947 0.8478 1.0005 1200
## scale(seasons)-grass_pickerel -0.2250 0.5627 -1.3558 -0.2285 0.9772 1.0049 1456
## scale(seasons)-greenside_darter -0.1770 0.5720 -1.2687 -0.1864 0.9002 1.0036 1200
## scale(seasons)-green_sunfish -0.4650 0.4276 -1.3411 -0.4635 0.3901 1.0090 1200
## scale(seasons)-johnny_darter 0.2365 0.3971 -0.5194 0.2240 1.0614 1.0030 1200
## scale(seasons)-largemouth_bass -0.2092 0.5748 -1.3042 -0.2179 0.9684 1.0012 1200
## scale(seasons)-longear_sunfish -0.1910 0.5681 -1.3383 -0.1999 0.8986 1.0004 1200
## scale(seasons)-longnose_dace -0.2145 0.5552 -1.3541 -0.1995 0.8626 1.0143 1200
## scale(seasons)-mottled_sculpin -0.1825 0.5679 -1.3525 -0.1805 0.9658 1.0015 1200
## scale(seasons)-northern_hog_sucker -0.1750 0.5581 -1.2779 -0.1885 0.9522 1.0024 1200
## scale(seasons)-pumpkinseed_sunfish -0.2287 0.5496 -1.3599 -0.2219 0.8735 1.0009 1301
## scale(seasons)-rainbow_darter -0.4499 0.4061 -1.2638 -0.4501 0.2832 0.9999 1200
## scale(seasons)-rainbow_trout -0.2272 0.5411 -1.2770 -0.2079 0.7987 1.0051 1200
## scale(seasons)-redbelly_dace -0.8027 0.4468 -1.7670 -0.7867 0.0406 1.0040 1197
## scale(seasons)-redside_dace -0.5211 0.4865 -1.5157 -0.5039 0.3817 1.0036 1200
## scale(seasons)-river_chub -0.2090 0.5561 -1.3016 -0.1899 0.7884 1.0006 1409
## scale(seasons)-silverjaw_minnow -0.1980 0.5581 -1.2958 -0.2097 0.8794 1.0305 1200
## scale(seasons)-smallmouth_bass -0.2192 0.5509 -1.3127 -0.2253 0.8644 1.0054 1200
## scale(seasons)-spotfin_shiner -0.1884 0.5550 -1.2859 -0.2006 0.8651 1.0091 1200
## scale(seasons)-stonecat_madtom -0.1828 0.5719 -1.2739 -0.1694 0.9372 1.0010 1200
## scale(seasons)-striped_shiner -0.1888 0.5716 -1.3014 -0.1868 0.9123 1.0042 1200
## scale(seasons)-trout_perch -0.2159 0.5727 -1.3134 -0.2150 0.9090 1.0041 1200
## scale(seasons)-warmouth_sunfish -0.2058 0.5612 -1.2884 -0.2043 0.9066 1.0035 1200
## scale(seasons)-white_sucker -0.3334 0.4030 -1.1594 -0.3305 0.4325 1.0419 1232
## scale(seasons)-yellow_bullhead_catfish -0.3894 0.5209 -1.4421 -0.3914 0.6377 1.0091 1200
## scale(seasons)-sessile_animals -0.2203 0.4234 -1.0867 -0.2137 0.5666 1.0046 1105
## scale(seasons)-aquatic_worms -0.0254 0.1032 -0.2166 -0.0270 0.1857 1.0004 1416
## scale(seasons)-sow_bugs 0.4887 0.1714 0.1753 0.4857 0.8304 1.0028 1200
## scale(seasons)-scuds 0.4519 0.1545 0.1566 0.4450 0.7785 1.0039 1200
## scale(seasons)-water_mites 0.3590 0.1514 0.0572 0.3582 0.6615 1.0081 964
## scale(seasons)-damselfly_nymphs -0.0906 0.1143 -0.3207 -0.0884 0.1280 1.0099 1200
## scale(seasons)-alderfly_larvae -0.1277 0.1398 -0.3931 -0.1263 0.1430 1.0072 1049
## scale(seasons)-other_beetles -0.4198 0.1113 -0.6419 -0.4167 -0.1981 1.0013 1200
## scale(seasons)-crayfish -0.4262 0.1184 -0.6579 -0.4290 -0.1926 1.0001 1200
## scale(seasons)-dragonfly_nymphs -0.1582 0.1028 -0.3585 -0.1558 0.0423 1.0044 1200
## scale(seasons)-riffle_beetles 0.0243 0.1128 -0.1960 0.0240 0.2419 1.0093 1200
## scale(seasons)-other_flies -0.1836 0.1355 -0.4516 -0.1817 0.0841 1.0125 822
## scale(seasons)-midges 0.3712 0.1343 0.1085 0.3654 0.6370 1.0015 974
## scale(seasons)-snails -0.1997 0.1134 -0.4207 -0.1976 0.0139 1.0044 1200
## scale(seasons)-clams -0.4151 0.1720 -0.7761 -0.4059 -0.1098 1.0050 1200
## scale(seasons)-fishfly_larvae -0.2194 0.1389 -0.4991 -0.2174 0.0586 1.0014 1100
## scale(seasons)-water_penny_beetles -0.0482 0.1617 -0.3495 -0.0532 0.2781 1.0032 1324
## scale(seasons)-cranefly_larvae 0.1343 0.1149 -0.1000 0.1345 0.3486 1.0045 1320
## scale(seasons)-ameletidae -1.8690 0.2363 -2.3641 -1.8555 -1.4522 1.0030 1056
## scale(seasons)-arthropleidae -0.1782 0.5551 -1.3165 -0.1598 0.8262 1.0004 1200
## scale(seasons)-baetidae 1.1663 0.1350 0.9063 1.1641 1.4353 1.0131 1200
## scale(seasons)-baetiscidae -0.2328 0.5699 -1.4133 -0.2439 0.9152 1.0008 1336
## scale(seasons)-caenidae 0.0762 0.2888 -0.5013 0.0732 0.6389 0.9998 1200
## scale(seasons)-ephemerellidae 0.8794 0.4324 0.0970 0.8606 1.7359 1.0327 1050
## scale(seasons)-ephemeridae 0.1353 0.4207 -0.6535 0.1192 1.0088 1.0082 1373
## scale(seasons)-heptageniidae 0.1134 0.1534 -0.1791 0.1145 0.4177 0.9998 1200
## scale(seasons)-isonychiidae -0.1834 0.5670 -1.2607 -0.1838 0.9895 1.0113 1200
## scale(seasons)-leptohyphidae -0.9456 0.3377 -1.7001 -0.9255 -0.3290 1.0037 1085
## scale(seasons)-leptophlebiidae -0.0300 0.1476 -0.3310 -0.0280 0.2508 1.0007 1253
## scale(seasons)-polymitarcyidae -0.1959 0.5548 -1.2278 -0.2084 0.9247 1.0168 1099
## scale(seasons)-potamanthidae -0.2074 0.5825 -1.3477 -0.2154 0.9372 1.0039 1200
## scale(seasons)-pseudironidae -0.2015 0.5770 -1.3225 -0.2226 0.9623 1.0122 1200
## scale(seasons)-siphlonuridae -0.2133 0.5811 -1.3364 -0.2185 1.0155 1.0030 1332
## scale(seasons)-capniidae -0.2050 0.5560 -1.3063 -0.2032 0.8588 1.0004 1051
## scale(seasons)-chloroperlidae -0.1170 0.1953 -0.4931 -0.1094 0.2842 1.0221 1037
## scale(seasons)-leuctridae 0.5099 0.1318 0.2532 0.5073 0.7619 1.0041 1200
## scale(seasons)-nemouridae -0.6125 0.1573 -0.9186 -0.6154 -0.3045 1.0109 1200
## scale(seasons)-peltoperlidae -0.2228 0.5684 -1.3217 -0.2198 0.8630 1.0165 1200
## scale(seasons)-perlidae -0.0860 0.1506 -0.3978 -0.0857 0.1949 1.0010 1200
## scale(seasons)-perlodidae -0.5449 0.2083 -0.9497 -0.5424 -0.1323 1.0080 1200
## scale(seasons)-pteronarcyidae -0.2161 0.5761 -1.3631 -0.2047 0.9229 1.0060 1200
## scale(seasons)-taeniopterygidae -0.1942 0.5933 -1.3449 -0.2018 0.9510 1.0031 1200
## scale(seasons)-brachycentridae -0.1882 0.5515 -1.3284 -0.1765 0.8874 1.0018 1200
## scale(seasons)-dipseuodopsidae -0.1888 0.5887 -1.3937 -0.1781 0.9565 1.0045 1200
## scale(seasons)-glossosomatidae 0.1690 0.4462 -0.6814 0.1673 1.0902 1.0041 1310
## scale(seasons)-goeridae -0.2325 0.5725 -1.3575 -0.2254 0.8548 1.0000 1362
## scale(seasons)-helicopsychidae -0.2187 0.5690 -1.3375 -0.1956 0.9149 1.0017 1200
## scale(seasons)-hydropsychidae 0.0524 0.1162 -0.1737 0.0535 0.2873 1.0009 1067
## scale(seasons)-hydroptilidae -0.1493 0.2418 -0.6221 -0.1514 0.3310 0.9992 979
## scale(seasons)-lepidostomatidae 0.0523 0.2054 -0.3378 0.0536 0.4569 1.0091 1325
## scale(seasons)-leptoceridae -0.1974 0.5887 -1.4024 -0.1935 0.9145 1.0016 1200
## scale(seasons)-limnephilidae -0.3237 0.1705 -0.6769 -0.3228 -0.0108 1.0012 1200
## scale(seasons)-molannidae 0.2643 0.4383 -0.5753 0.2704 1.1115 0.9993 1451
## scale(seasons)-odontoceridae -0.7630 0.4385 -1.6716 -0.7293 0.0072 1.0088 1200
## scale(seasons)-philopotamidae 0.2481 0.1293 -0.0111 0.2475 0.5075 1.0047 1348
## scale(seasons)-phryganeidae -0.6789 0.3720 -1.4360 -0.6616 -0.0108 1.0140 1086
## scale(seasons)-polycentropodidae 0.0126 0.1296 -0.2437 0.0132 0.2587 1.0084 1200
## scale(seasons)-psychomiidae -0.2188 0.5593 -1.3195 -0.2163 0.8131 1.0022 1391
## scale(seasons)-rhyacophilidae -0.3146 0.2048 -0.7099 -0.3173 0.1086 1.0037 1200
## scale(seasons)-uenoidae -0.7840 0.1723 -1.1208 -0.7851 -0.4561 1.0004 1200
## scale(dayyear)-streambiotic_suff_mtdusky -0.0321 0.2753 -0.5932 -0.0344 0.5000 1.0100 1200
## scale(dayyear)-streambiotic_suff_nodusky 0.0499 0.1602 -0.2626 0.0501 0.3702 1.0185 1042
## scale(dayyear)-streambiotic_suff_twolined -0.1209 0.1386 -0.4038 -0.1210 0.1425 1.0070 1200
## scale(dayyear)-streambiotic_suff_longtail 0.1009 0.3712 -0.6348 0.0923 0.8278 1.0284 1200
## scale(dayyear)-streambiotic_suff_red -0.1272 0.2860 -0.6782 -0.1388 0.4478 0.9998 1384
## scale(dayyear)-streambiotic_suff_mole 0.2099 0.4104 -0.6160 0.2130 1.0149 1.0094 1200
## scale(dayyear)-streambiotic_suff_fourtoed 0.0368 0.4507 -0.8928 0.0507 0.8822 1.0178 1200
## scale(dayyear)-streambiotic_suff_redback -0.0457 0.1276 -0.3051 -0.0465 0.2087 1.0036 1200
## scale(dayyear)-streambiotic_suff_slimy 0.7116 0.3430 0.0443 0.6925 1.4079 1.0024 1317
## scale(dayyear)-blacknose_dace 0.3673 0.2199 -0.0420 0.3643 0.8257 1.0147 1200
## scale(dayyear)-bluegill_sunfish -0.0125 0.4275 -0.8448 -0.0161 0.8175 1.0062 1200
## scale(dayyear)-bluntnose_minnow 0.0283 0.4585 -0.8361 0.0063 0.9576 1.0090 1200
## scale(dayyear)-brindled_madtom 0.0423 0.4414 -0.8365 0.0436 0.9358 1.0006 1200
## scale(dayyear)-brook_stickleback 0.0318 0.4475 -0.8470 0.0454 0.8753 1.0009 1344
## scale(dayyear)-brook_trout 0.0360 0.4451 -0.8447 0.0489 0.9078 0.9998 1200
## scale(dayyear)-central_mudminnow 0.0365 0.4321 -0.7992 0.0354 0.8907 1.0021 1200
## scale(dayyear)-central_stoneroller_minnow 0.3969 0.3139 -0.2193 0.4097 1.0365 0.9999 1200
## scale(dayyear)-common_shiner 0.0498 0.4022 -0.7111 0.0492 0.8339 1.0193 1200
## scale(dayyear)-creek_chub 0.3004 0.1766 -0.0263 0.2985 0.6645 1.0042 1200
## scale(dayyear)-eastern_sand_darter 0.0379 0.4428 -0.7841 0.0407 0.8883 1.0086 1352
## scale(dayyear)-fantail_darter 0.0441 0.4423 -0.8516 0.0500 0.9089 1.0039 1200
## scale(dayyear)-fathead_minnow 0.0458 0.4378 -0.8104 0.0662 0.9132 1.0035 1200
## scale(dayyear)-golden_shiner 0.0180 0.4378 -0.8596 0.0323 0.8989 1.0076 1200
## scale(dayyear)-goldfish 0.0278 0.4706 -0.8897 0.0287 0.9545 1.0070 1746
## scale(dayyear)-grass_pickerel 0.0446 0.4384 -0.8284 0.0453 0.8766 1.0073 1200
## scale(dayyear)-greenside_darter 0.0318 0.4379 -0.8098 0.0274 0.9043 1.0006 1365
## scale(dayyear)-green_sunfish 0.2658 0.3415 -0.4095 0.2690 0.9266 1.0173 1200
## scale(dayyear)-johnny_darter 0.5204 0.3403 -0.1183 0.5207 1.2014 0.9993 1123
## scale(dayyear)-largemouth_bass 0.0390 0.4504 -0.8575 0.0287 0.9314 1.0087 1200
## scale(dayyear)-longear_sunfish 0.0534 0.4282 -0.7677 0.0562 0.9121 1.0136 1200
## scale(dayyear)-longnose_dace 0.0401 0.4472 -0.8599 0.0243 0.9490 1.0161 1200
## scale(dayyear)-mottled_sculpin 0.0417 0.4381 -0.8350 0.0508 0.8701 1.0207 971
## scale(dayyear)-northern_hog_sucker 0.0297 0.4399 -0.8340 0.0236 0.8832 1.0006 1649
## scale(dayyear)-pumpkinseed_sunfish 0.0476 0.4354 -0.8374 0.0644 0.8511 1.0094 1200
## scale(dayyear)-rainbow_darter 0.0305 0.3735 -0.7582 0.0367 0.7180 1.0017 1200
## scale(dayyear)-rainbow_trout 0.0323 0.4279 -0.8064 0.0477 0.8870 1.0119 1200
## scale(dayyear)-redbelly_dace -0.1027 0.3875 -0.8651 -0.0969 0.7023 1.0051 1080
## scale(dayyear)-redside_dace 0.1096 0.4208 -0.6831 0.0921 0.9359 1.0107 1200
## scale(dayyear)-river_chub 0.0218 0.4279 -0.7867 0.0152 0.8805 1.0090 1200
## scale(dayyear)-silverjaw_minnow 0.0223 0.4480 -0.8527 0.0272 0.9186 1.0033 1200
## scale(dayyear)-smallmouth_bass 0.0725 0.4519 -0.8383 0.0758 0.9588 1.0014 1423
## scale(dayyear)-spotfin_shiner 0.0373 0.4566 -0.8989 0.0339 0.9087 1.0000 1200
## scale(dayyear)-stonecat_madtom 0.0437 0.4501 -0.8791 0.0456 0.9565 1.0223 1200
## scale(dayyear)-striped_shiner 0.0499 0.4411 -0.7971 0.0529 0.9139 1.0102 1211
## scale(dayyear)-trout_perch 0.0454 0.4335 -0.7786 0.0321 0.9228 1.0142 1200
## scale(dayyear)-warmouth_sunfish 0.0535 0.4341 -0.7892 0.0514 0.9213 1.0002 1200
## scale(dayyear)-white_sucker 0.2139 0.3605 -0.4607 0.2222 0.8887 1.0089 1200
## scale(dayyear)-yellow_bullhead_catfish 0.1972 0.4140 -0.6301 0.1911 0.9962 1.0022 1200
## scale(dayyear)-sessile_animals 0.4860 0.3716 -0.2202 0.4578 1.2716 1.0008 1200
## scale(dayyear)-aquatic_worms -0.1231 0.1024 -0.3165 -0.1299 0.0822 1.0004 1326
## scale(dayyear)-sow_bugs 0.1291 0.1679 -0.2126 0.1288 0.4521 1.0012 1009
## scale(dayyear)-scuds -0.0252 0.1510 -0.3190 -0.0184 0.2799 1.0078 1200
## scale(dayyear)-water_mites 0.0071 0.1444 -0.2845 0.0052 0.2999 1.0121 1200
## scale(dayyear)-damselfly_nymphs 0.1964 0.1140 -0.0175 0.1961 0.4195 1.0024 1200
## scale(dayyear)-alderfly_larvae 0.6684 0.1569 0.3802 0.6661 0.9709 1.0002 1200
## scale(dayyear)-other_beetles -0.0802 0.1063 -0.3009 -0.0781 0.1320 1.0009 1313
## scale(dayyear)-crayfish -0.0759 0.1169 -0.3018 -0.0755 0.1596 1.0037 1307
## scale(dayyear)-dragonfly_nymphs 0.0938 0.1085 -0.1119 0.0941 0.3064 1.0069 1200
## scale(dayyear)-riffle_beetles 0.1416 0.1251 -0.0959 0.1398 0.3945 1.0088 1200
## scale(dayyear)-other_flies -0.1825 0.1317 -0.4343 -0.1805 0.0770 0.9993 1200
## scale(dayyear)-midges -0.3404 0.1219 -0.5748 -0.3381 -0.1112 1.0011 1200
## scale(dayyear)-snails 0.1449 0.1088 -0.0625 0.1468 0.3529 1.0096 1200
## scale(dayyear)-clams 0.0302 0.1484 -0.2587 0.0395 0.3065 1.0017 1200
## scale(dayyear)-fishfly_larvae 0.4734 0.1533 0.1884 0.4689 0.7879 1.0075 1200
## scale(dayyear)-water_penny_beetles 0.8444 0.1955 0.4670 0.8439 1.2312 1.0024 1200
## scale(dayyear)-cranefly_larvae 0.4796 0.1270 0.2272 0.4777 0.7300 1.0013 1200
## scale(dayyear)-ameletidae -0.2326 0.1439 -0.5303 -0.2296 0.0423 1.0330 1200
## scale(dayyear)-arthropleidae 0.0460 0.4582 -0.9065 0.0437 0.9271 1.0185 1330
## scale(dayyear)-baetidae 0.0852 0.1294 -0.1592 0.0799 0.3447 1.0060 1200
## scale(dayyear)-baetiscidae 0.0434 0.4293 -0.8084 0.0446 0.8505 1.0001 1200
## scale(dayyear)-caenidae 0.0681 0.2899 -0.4970 0.0670 0.6226 1.0082 1200
## scale(dayyear)-ephemerellidae -0.5367 0.3675 -1.2828 -0.5315 0.1724 1.0045 1535
## scale(dayyear)-ephemeridae 0.1991 0.3584 -0.4911 0.1913 0.9245 1.0002 1200
## scale(dayyear)-heptageniidae -0.2386 0.1735 -0.5819 -0.2396 0.0986 1.0020 1200
## scale(dayyear)-isonychiidae 0.0383 0.4143 -0.7572 0.0357 0.8419 0.9996 1067
## scale(dayyear)-leptohyphidae 0.0671 0.2422 -0.4209 0.0655 0.5178 1.0006 1200
## scale(dayyear)-leptophlebiidae -0.4363 0.1666 -0.7666 -0.4321 -0.1046 1.0118 1200
## scale(dayyear)-polymitarcyidae 0.0530 0.4488 -0.8100 0.0466 0.9563 1.0035 1200
## scale(dayyear)-potamanthidae 0.0422 0.4532 -0.8594 0.0431 0.9825 1.0067 1200
## scale(dayyear)-pseudironidae 0.0367 0.4435 -0.8053 0.0282 0.9233 1.0045 1200
## scale(dayyear)-siphlonuridae 0.0341 0.4387 -0.8119 0.0203 0.9213 1.0020 1294
## scale(dayyear)-capniidae 0.0440 0.4413 -0.8476 0.0490 0.9335 1.0022 1200
## scale(dayyear)-chloroperlidae -0.3078 0.2210 -0.7341 -0.3149 0.1131 1.0011 1200
## scale(dayyear)-leuctridae -0.2411 0.1513 -0.5370 -0.2423 0.0524 1.0048 1012
## scale(dayyear)-nemouridae -0.8215 0.1863 -1.2043 -0.8107 -0.4872 0.9991 1200
## scale(dayyear)-peltoperlidae 0.0146 0.4226 -0.8292 0.0143 0.8263 1.0044 1200
## scale(dayyear)-perlidae -0.1291 0.1727 -0.4789 -0.1341 0.1915 1.0069 1200
## scale(dayyear)-perlodidae -1.1846 0.2726 -1.7471 -1.1712 -0.6809 1.0019 1200
## scale(dayyear)-pteronarcyidae 0.0501 0.4527 -0.8144 0.0373 0.9270 1.0048 1200
## scale(dayyear)-taeniopterygidae 0.0445 0.4493 -0.8697 0.0435 0.9719 1.0057 1200
## scale(dayyear)-brachycentridae 0.0303 0.4342 -0.8231 0.0377 0.8682 1.0088 1306
## scale(dayyear)-dipseuodopsidae 0.0372 0.4263 -0.8309 0.0280 0.8639 1.0052 1200
## scale(dayyear)-glossosomatidae -0.1751 0.4264 -1.0007 -0.1629 0.6160 1.0102 1183
## scale(dayyear)-goeridae 0.0395 0.4544 -0.8619 0.0224 0.9212 1.0086 1292
## scale(dayyear)-helicopsychidae 0.0421 0.4338 -0.8402 0.0453 0.8570 1.0004 1319
## scale(dayyear)-hydropsychidae 0.4611 0.1380 0.2039 0.4600 0.7477 1.0085 1560
## scale(dayyear)-hydroptilidae 0.1053 0.2141 -0.3163 0.0981 0.5156 1.0009 1200
## scale(dayyear)-lepidostomatidae -0.3813 0.2222 -0.8409 -0.3724 0.0459 1.0134 1071
## scale(dayyear)-leptoceridae 0.0282 0.4362 -0.8375 0.0301 0.8929 1.0047 1399
## scale(dayyear)-limnephilidae 0.1171 0.1642 -0.1886 0.1156 0.4375 1.0226 1350
## scale(dayyear)-molannidae 0.0216 0.3916 -0.7077 0.0236 0.7653 1.0017 1200
## scale(dayyear)-odontoceridae 0.1587 0.3948 -0.5830 0.1429 0.9929 1.0018 1093
## scale(dayyear)-philopotamidae 0.2924 0.1414 0.0039 0.2941 0.5570 1.0230 1062
## scale(dayyear)-phryganeidae 0.5464 0.3163 -0.0064 0.5315 1.1936 1.0041 1200
## scale(dayyear)-polycentropodidae 0.2357 0.1426 -0.0469 0.2387 0.5121 0.9989 1200
## scale(dayyear)-psychomiidae 0.0496 0.4368 -0.8491 0.0498 0.8901 0.9999 892
## scale(dayyear)-rhyacophilidae -0.4950 0.2213 -0.9482 -0.4893 -0.0839 1.0030 1200
## scale(dayyear)-uenoidae -0.3711 0.1622 -0.6941 -0.3665 -0.0588 1.0054 1200Joint species distribution models
phwhall_occobject_2<-phwhall_occobject
phwhall_occobject_2$y<-phwhall_occobject$y[(apply(phwhall_occobject$y, 1, mean, na.rm = TRUE) > 0),,]
dim(phwhall_occobject_2$y)
sppnames_2<-rownames(phwhall_occobject_2$y)
sort(apply(phwhall_occobject_2$y, 1, mean, na.rm = TRUE))
# louvain's cluster identified 3 clusters - one of (perhaps) more rocky stream communities, one with most fish that are indicators of # stream health, and another of more spring-associated communities.
#Place a common species first. The first species has all of its factor loadings set to fixed values, and so it can have a large #influence on the resulting interpretation on the factor loadings and latent factors. We have also found that having a very rare #species first can result in very slow mixing and increased sensitivity to initial values of the latent factor loadings matrix.
#For the remaining q−1 factors, place species that you believe will show different occurrence patterns than the first species, and #the other species placed before it. Place these remaining q−1 species in order of decreasing differences from the initial factor.
#For example, if I had q=3, for the second species in the array, I would place a species that I a priori think is most different from #the first species. For the third species in the array, I would place a species that I think will show different occurrence patterns #than both the first and second species, but its patterns may not be as noticeably different compared to the first and second species.
# Reorder species.
sp.ordered <- c("streambiotic_suff_twolined","rainbow_darter","odontoceridae",sppnames_2[!(sppnames_2 %in% c("streambiotic_suff_twolined","rainbow_darter","odontoceridae"))])
#
# Create new detection-nondetection data matrix in the new order
y.new <- phwhall_occobject_2$y[sp.ordered, , ]
# Create a new data array
phwhall.ordered <- phwhall_occobject_2
# Change the data to the new ordered data
phwhall.ordered$y <- y.new
str(phwhall.ordered)
# Number of species
N <- nrow(phwhall.ordered$y)
# Initiate all lambda initial values to 0.
n.factors<-3
lambda.inits <- matrix(0, N, n.factors)
# Set diagonal elements to 1
diag(lambda.inits) <- 1
# Set lower triangular elements to random values from a standard normal distribution
lambda.inits[lower.tri(lambda.inits)] <- rnorm(sum(lower.tri(lambda.inits)))
# Check it out. Note this is also how spOccupancy specifies default
# initial values for lambda.
lambda.inits
inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
lambda = lambda.inits,
z = apply(phwhall.ordered$y, c(1, 2), max, na.rm = TRUE))
priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
n.samples <- 5000
n.burn <- 1000
n.thin <- 8
n.chains <- 3
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_dayyearseason.det.formula <- ~scale(seasons)+scale(dayyear)
out.lfMsPGOcc <- lfMsPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula, det.formula = phwhall_occobject_dayyearseason.det.formula,
data = phwhall.ordered, inits = inits, priors = priors,
n.factors = n.factors, n.samples = n.samples,
n.omp.threads = 1, verbose = TRUE, n.report = 1000,
n.burn = n.burn, n.thin = n.thin, n.chains = n.chains)
summary(out.lfMsPGOcc)
plot(out.lfMsPGOcc$beta.comm.samples, density = FALSE)
summary(out.lfMsPGOcc$lambda.samples)
ppc.out <- ppcOcc(out.lfMsPGOcc, fit.stat = 'freeman-tukey', group = 1)
options(max.print=1000000)
sink("out_lambdas.txt")
print(summary(out.lfMsPGOcc$lambda.samples,quantiles=c(0.05,0.5,0.95)))
sink()
write.csv(summary(out.lfMsPGOcc$lambda.samples),"out.csv")now spatial model
# Pair-wise distance between all sites
dist.phwh <- dist(phwhall_occobject_2$coords)
# Exponential correlation model
cov.model <- "exponential"
# Specify all other initial values identical to lfMsPGOcc() from before
# Number of species
N <- nrow(phwhall_occobject_2$y)
# Initiate all lambda initial values to 0.
lambda.inits <- matrix(0, N, n.factors)
# Set diagonal elements to 1
diag(lambda.inits) <- 1
# Set lower triangular elements to random values from a standard normal dist
lambda.inits[lower.tri(lambda.inits)] <- rnorm(sum(lower.tri(lambda.inits)))
# Check it out
lambda.inits
inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
lambda = lambda.inits,
phi = 3 / mean(dist.phwh),
z = apply(phwhall.ordered$y, c(1, 2), max, na.rm = TRUE))
batch.length <- 25
n.batch <- 200
n.burn <- 3000
n.thin <- 2
n.chains <- 3
min.dist <- min(dist.phwh)
max.dist <- max(dist.phwh)
priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1),
phi.unif = list(3 / max.dist, 3 / min.dist))
tuning <- list(phi = 1)
n.omp.threads <- 1
verbose <- TRUE
n.report <- 50 # Report progress at every 50th batch.
out.sfMsPGOcc <- sfMsPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula, det.formula = phwhall_occobject_dayyearseason.det.formula,
data = phwhall.ordered,
inits = inits,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
priors = priors,
n.factors = n.factors,
cov.model = cov.model,
tuning = tuning,
n.omp.threads = n.omp.threads,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
n.report = n.report,
n.burn = n.burn,
n.thin = n.thin,
n.chains = n.chains)
summary(out.sfMsPGOcc)
plot(out.sfMsPGOcc$beta.comm.samples, density = FALSE)
summary(out.sfMsPGOcc$lambda.samples)
ppc.out.sf <- ppcOcc(out.sfMsPGOcc, fit.stat = 'freeman-tukey', group = 1)
waicOcc(out.sfMsPGOcc)
waicOcc(out.lfMsPGOcc)
lambda.inits.1 <- matrix(0, N, 1)
# Set diagonal elements to 1
diag(lambda.inits.1) <- 1
# Set lower triangular elements to random values from a standard normal dist
lambda.inits.1[lower.tri(lambda.inits.1)] <- rnorm(sum(lower.tri(lambda.inits.1)))
# Check it out
lambda.inits.1
inits.1 <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
lambda = lambda.inits.1,
phi = 3 / mean(dist.phwh),
z = apply(phwhall.ordered$y, c(1, 2), max, na.rm = TRUE))
out.sfMsPGOcc.fac1 <- sfMsPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula, det.formula = phwhall_occobject_dayyearseason.det.formula,
data = phwhall.ordered,
inits = inits.1,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
priors = priors,
n.factors = 1,
cov.model = cov.model,
tuning = tuning,
n.omp.threads = n.omp.threads,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
n.report = n.report,
n.burn = n.burn,
n.thin = n.thin,
n.chains = n.chains)
summary(out.sfMsPGOcc.fac1$lambda.samples)This revised model (seems) to point towards sow bugs as particularly differentiated from good stream salamander habitat. Let’s explore that further - using sow bugs as the second factor.
# Reorder species.
sp.ordered.2 <- c("streambiotic_suff_twolined","sow_bugs",sppnames_2[!(sppnames_2 %in% c("streambiotic_suff_twolined","sow_bugs"))])
#
# Create new detection-nondetection data matrix in the new order
y.new.2 <- phwhall_occobject_2$y[sp.ordered.2, , ]
# Create a new data array
phwhall.ordered.2 <- phwhall_occobject_2
# Change the data to the new ordered data
phwhall.ordered.2$y <- y.new.2
str(phwhall.ordered.2)
# Number of species
N <- nrow(phwhall.ordered.2$y)
# Initiate all lambda initial values to 0.
n.factors<-2
lambda.inits <- matrix(0, N, n.factors)
# Set diagonal elements to 1
diag(lambda.inits) <- 1
# Set lower triangular elements to random values from a standard normal distribution
lambda.inits[lower.tri(lambda.inits)] <- rnorm(sum(lower.tri(lambda.inits)))
# Check it out. Note this is also how spOccupancy specifies default
# initial values for lambda.
lambda.inits
n.samples <- 5000
n.burn <- 1000
n.thin <- 8
n.chains <- 3
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_dayyearseason.det.formula <- ~scale(seasons)+scale(dayyear)
inits.2 <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
lambda = lambda.inits,
phi = 3 / mean(dist.phwh),
z = apply(phwhall.ordered.2$y, c(1, 2), max, na.rm = TRUE))
out.sfMsPGOcc.fac2 <- sfMsPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula, det.formula = phwhall_occobject_dayyearseason.det.formula,
data = phwhall.ordered.2,
inits = inits.2,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
priors = priors,
n.factors = n.factors,
cov.model = cov.model,
tuning = tuning,
n.omp.threads = n.omp.threads,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
n.report = n.report,
n.burn = n.burn,
n.thin = n.thin,
n.chains = n.chains)
summary(out.sfMsPGOcc.fac2$lambda.samples)
waicOcc(out.sfMsPGOcc.fac1)
waicOcc(out.sfMsPGOcc.fac2)Nope - q = 1 is still best.
out.sfMsPGOcc.fac1 should likely be the model to use for now.
reasonable fit for this model already! let’s try to predict out to all streams.
phwh_subset_2<-phwh_subset %>% dplyr::select(stream_name,date,latitude,longitude,pool_depth_.cm.,per_sand,per_cobble,substrate_metric_total,pool_metric_total) %>% distinct(stream_name, date, .keep_all = TRUE)
phwh_subset_3<-na.omit(phwh_subset_2)
pool_depth_.cm. = ((phwh_subset_3$pool_depth_.cm. - mean(phwhall.ordered$occ.covs[,16]))/sd(phwhall.ordered$occ.covs[,16]))
per_sand = ((phwh_subset_3$per_sand - mean(phwhall.ordered$occ.covs[,8]))/sd(phwhall.ordered$occ.covs[,8]))
per_cobble = ((phwh_subset_3$per_cobble - mean(phwhall.ordered$occ.covs[, 6])) / sd(phwhall.ordered$occ.covs[, 6]))
substrate_metric_total = ((phwh_subset_3$substrate_metric_total - mean(phwhall.ordered$occ.covs[, 1])) / sd(phwhall.ordered$occ.covs[, 1]))
pool_metric_total = ((as.numeric(phwh_subset_3$pool_metric_total) - mean(phwhall.ordered$occ.covs[, 15])) / sd(phwhall.ordered$occ.covs[, 15]))
predict_data<-data.frame(int = 1, pool_depth_.cm. = pool_depth_.cm., per_sand = per_sand, per_cobble = per_cobble, substrate_metric_total = substrate_metric_total, pool_metric_total = pool_metric_total)
coords<-as.matrix(phwh_subset_3[,c("longitude","latitude")])
fullphwhpred<-predict(out.sfMsPGOcc.fac1,as.matrix(predict_data),coords,type="occupancy")
str(fullphwhpred)
# Species richness samples
rich.pred <- apply(fullphwhpred$z.0.samples, c(1, 3), sum)
rich.real<-apply(na.omit(phwhall_occobject$y),c(2,3),function(x) sum(x,na.rm=T))
latersurveys<-rich.real[,3:8]
latersurveys[latersurveys==0]<-NA
rich.real[,3:8]<-latersurveys
plot.dat <- data.frame(stream_name=phwh_subset_3$stream_name,
date=phwh_subset_3$date,
x = phwh_subset_3$longitude,
y = phwh_subset_3$latitude,
rich.mean = apply(rich.pred, 2, mean),
rich.sd = apply(rich.pred, 2, sd))
plot.dat.real <- data.frame(stream_name=colnames(seventhcheck_all_2),
x = phwhall_occobject$coords[,1],
y = phwhall_occobject$coords[,2],
rich.mean = apply(rich.real, 1, function(x) mean(x,na.rm=T)),
rich.sd = apply(rich.real, 1, function(x) sd(x,na.rm=T)))
# Plot species richness across PHWH
RMSE <- function(observed, predicted) {
sqrt(mean((predicted - observed)^2, na.rm=TRUE))
}
f1 <- function(x, test, train) {
nmx <- x[1]
idp <- x[2]
if (nmx < 1) return(Inf)
if (idp < .001) return(Inf)
m <- gstat(formula=rich.mean~1, locations=train, nmax=nmx, set=list(idp=idp))
p <- predict(m, newdata=test, debug.level=0)$var1.pred
RMSE(test$rich.mean, p)
}
set.seed(12345)
respoly<-readOGR("G:/NaturalResources/Byer/DataAnalysis/bbs_cmp_analyses/bbsapp/CMPboundaries.shp")
respoly <- spTransform(respoly, CRS("+init=epsg:4326"))
plot.dat$date<-as.Date(plot.dat$date , format = "%Y/%m/%d")
plot.dat$year<-year(plot.dat$date)
sort(unique(plot.dat$year))
plot.dat_20032010<-plot.dat[plot.dat$year %in% c(2003,2004,2007,2008,20009,2010),]
plot.dat_20172022<-plot.dat[plot.dat$year %in% c(2017,2018,2019,2020,2021,2022),]
dsp <- SpatialPoints(plot.dat_20172022[,3:4])
dsp <- SpatialPointsDataFrame(dsp, plot.dat_20172022)
crs(dsp)<-CRS("+init=epsg:4326")
j <- sample(nrow(dsp), 0.2 * nrow(dsp))
tst <- dsp[j,]
trn <- dsp[-j,]
opt <- optim(c(8, .5), f1, test=tst, train=trn)
# define groups for mapping
cuts <- c(14,21,28,35)
# set up a palette of interpolated colors
blues <- colorRampPalette(c('yellow', 'orange', 'blue', 'dark blue'))
spplot(dsp, 'rich.mean', cuts=cuts, col.regions=blues(5), pch=20, cex=2)
v <- voronoi(dsp)
ch <- chull(plot.dat_20172022[,3:4])
coords <- plot.dat_20172022[c(ch, ch[1]), 3:4] # closed polygon
plot(plot.dat_20172022[,3:4], pch=19)
lines(coords, col="red")
vca <- intersect(v, respoly)
spplot(vca, 'rich.mean', col.regions=rev(get_col_regions()))
r <- raster(respoly, res=0.0001)
crs(r)<-CRS("+init=epsg:4326")
crs(vca)<-CRS("+init=epsg:4326")
vr <- rasterize(vca, r, 'rich.mean')
crs(vr)<-CRS("+init=epsg:4326")
plot(vr)
points(dsp)
writeRaster(vr,filename = paste0(getwd(),"richness_20172022.asc"),overwrite=TRUE)
#actual richness
dsp <- SpatialPoints(plot.dat.real[,2:3])
dsp <- SpatialPointsDataFrame(dsp, plot.dat.real)
crs(dsp)<-CRS("+init=epsg:3734")
j <- sample(nrow(dsp), 0.2 * nrow(dsp))
tst <- dsp[j,]
trn <- dsp[-j,]
opt <- optim(c(8, .5), f1, test=tst, train=trn)
respoly.3734<-spTransform(respoly,CRS("+init=epsg:3734"))
r <- raster(respoly.3734, res=300)
fitmax <- gstat::gstat(formula = rich.mean ~ 1, data = dsp, nmax = opt$par[1], set = list(idp = opt$par[2]))
maxint <- raster::interpolate(r, model=fitmax)
crs(maxint)<-CRS("+init=epsg:3734")
plot(maxint, col=rev(heat.colors(255)))
r2 <- crop(maxint, extent(respoly.3734))
r3 <- mask(r2, respoly.3734)
plot(r3, col=rev(heat.colors(255)))
writeRaster(r3,filename = paste0(getwd(),"richness_observed.asc"),overwrite=TRUE)As a brief note - all of the above assumes closure (no immigration/emigration across surveys), which is likely violated. There are two suites of approaches that could be used at this juncture - Hmsc, assuming perfect detection, and Fidino et al. (2019)’s dcom. First, I will try just fitting the spatial factor occupancy model to 2008 data.
phwhall_occobject_2008_2<-phwhall_occobject_2008
phwhall_occobject_2008_2$y<-phwhall_occobject_2008$y[(apply(phwhall_occobject_2008$y, 1, mean, na.rm = TRUE) > 0),,]
dim(phwhall_occobject_2008_2$y)
sppnames_2008_2<-rownames(phwhall_occobject_2008_2$y)
sort(apply(phwhall_occobject_2008_2$y, 1, mean, na.rm = TRUE))
# louvain's cluster identified 3 clusters - one of (perhaps) more rocky stream communities, one with most fish that are indicators of # stream health, and another of more spring-associated communities.
#Place a common species first. The first species has all of its factor loadings set to fixed values, and so it can have a large #influence on the resulting interpretation on the factor loadings and latent factors. We have also found that having a very rare #species first can result in very slow mixing and increased sensitivity to initial values of the latent factor loadings matrix.
#For the remaining q−1 factors, place species that you believe will show different occurrence patterns than the first species, and #the other species placed before it. Place these remaining q−1 species in order of decreasing differences from the initial factor.
#For example, if I had q=3, for the second species in the array, I would place a species that I a priori think is most different from #the first species. For the third species in the array, I would place a species that I think will show different occurrence patterns #than both the first and second species, but its patterns may not be as noticeably different compared to the first and second species.
# Reorder species.
sp.2008.ordered <- c("midges","streambiotic_suff_mole","johnny_darter",sppnames_2008_2[!(sppnames_2008_2 %in% c("midges","streambiotic_suff_mole","johnny_darter"))])
# Create new detection-nondetection data matrix in the new order
y.2008.new <- phwhall_occobject_2008_2$y[sp.2008.ordered, , ]
# Create a new data array
phwhall.2008.ordered <- phwhall_occobject_2008_2
# Change the data to the new ordered data
phwhall.2008.ordered$y <- y.2008.new
str(phwhall.2008.ordered)
# Pair-wise distance between all sites
dist.phwh <- dist(phwhall.2008.ordered$coords)
# Exponential correlation model
cov.model <- "exponential"
# Specify all other initial values identical to lfMsPGOcc() from before
# Number of species
N <- nrow(phwhall.2008.ordered$y)
# Initiate all lambda initial values to 0.
lambda.inits <- matrix(0, N, n.factors)
# Set diagonal elements to 1
diag(lambda.inits) <- 1
# Set lower triangular elements to random values from a standard normal dist
lambda.inits[lower.tri(lambda.inits)] <- rnorm(sum(lower.tri(lambda.inits)))
# Check it out
lambda.inits
inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
lambda = lambda.inits,
phi = 3 / mean(dist.phwh),
z = apply(phwhall.2008.ordered$y, c(1, 2), max, na.rm = TRUE))
batch.length <- 50
n.batch <- 500
n.burn <- 5000
n.thin <- 2
n.chains <- 3
min.dist <- min(dist.phwh)
max.dist <- max(dist.phwh)
priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1),
phi.unif = list(3 / max.dist, 3 / min.dist))
tuning <- list(phi = 1)
n.omp.threads <- 1
verbose <- TRUE
n.report <- 50 # Report progress at every 50th batch.
phwhall_occobject_2008_allsigcor.occ.formula <- ~scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_2008_dayyearseason.det.formula <- ~scale(dayyear)
out.sfMsPGOcc.2008 <- sfMsPGOcc(occ.formula = phwhall_occobject_2008_allsigcor.occ.formula, det.formula = phwhall_occobject_2008_dayyearseason.det.formula,
data = phwhall.2008.ordered,
inits = inits,
n.batch = n.batch,
batch.length = batch.length,
accept.rate = 0.43,
priors = priors,
n.factors = n.factors,
cov.model = cov.model,
tuning = tuning,
n.omp.threads = n.omp.threads,
verbose = TRUE,
NNGP = TRUE,
n.neighbors = 5,
n.report = n.report,
n.burn = n.burn,
n.thin = n.thin,
n.chains = n.chains)
summary(out.sfMsPGOcc.2008)
plot(out.sfMsPGOcc.2008$beta.comm.samples, density = FALSE)
summary(out.sfMsPGOcc.2008$lambda.samples)
ppc.out.2008 <- ppcOcc(out.sfMsPGOcc.2008, fit.stat = 'freeman-tukey', group = 1)
summary(ppc.out.2008)improved detection model - for 2008 alone
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_dayyear.det.formula <- ~scale(dayyear)
# Number of species
N <- dim(phwhall.2008.ordered$y)[1]
# Distances between sites
dist.phwh <- dist(phwhall.2008.ordered$coords)
# Exponential covariance model
cov.model <- "exponential"
ms.inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(phwhall.2008.ordered$y, c(1, 2), max, na.rm = TRUE))
ms.priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
out.alltax.allsigcor.impdet.ms.2008 <- msPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula,
det.formula = phwhall_occobject_dayyear.det.formula,
data = phwhall.2008.ordered,
inits = ms.inits,
n.samples = 30000,
priors = ms.priors,
n.omp.threads = 4,
verbose = TRUE,
n.report = 10000,
n.burn = 5000,
n.thin = 50,
n.chains = 3)
summary(out.alltax.allsigcor.impdet.ms.2008, level = 'both')
ppc.alltax.allsigcor.impdet.ms.2008.out <- ppcOcc(out.alltax.allsigcor.impdet.ms.2008, 'chi-squared', group = 1)
summary(ppc.alltax.allsigcor.impdet.ms.2008.out, level = 'both')
waicOcc(out.alltax.allsigcor.impdet.ms.2008)
write_rds(out.alltax.allsigcor.impdet.ms,"alltaxmodel_phwh.rds")occupancy model for 0710 alone
phwhall_occobject_0710_2<-phwhall_occobject_0710
phwhall_occobject_0710_2$y<-phwhall_occobject_0710$y[(apply(phwhall_occobject_0710$y, 1, mean, na.rm = TRUE) > 0),,]
dim(phwhall_occobject_0710_2$y)
sppnames_0710_2<-rownames(phwhall_occobject_0710_2$y)
sort(apply(phwhall_occobject_0710_2$y, 1, mean, na.rm = TRUE))
# louvain's cluster identified 3 clusters - one of (perhaps) more rocky stream communities, one with most fish that are indicators of # stream health, and another of more spring-associated communities.
#Place a common species first. The first species has all of its factor loadings set to fixed values, and so it can have a large #influence on the resulting interpretation on the factor loadings and latent factors. We have also found that having a very rare #species first can result in very slow mixing and increased sensitivity to initial values of the latent factor loadings matrix.
#For the remaining q−1 factors, place species that you believe will show different occurrence patterns than the first species, and #the other species placed before it. Place these remaining q−1 species in order of decreasing differences from the initial factor.
#For example, if I had q=3, for the second species in the array, I would place a species that I a priori think is most different from #the first species. For the third species in the array, I would place a species that I think will show different occurrence patterns #than both the first and second species, but its patterns may not be as noticeably different compared to the first and second species.
# Reorder species.
sp.0710.ordered <- c("midges","streambiotic_suff_mole","johnny_darter",sppnames_0710_2[!(sppnames_0710_2 %in% c("midges","streambiotic_suff_mole","johnny_darter"))])
# Create new detection-nondetection data matrix in the new order
y.0710.new <- phwhall_occobject_0710_2$y[sp.0710.ordered, , ]
# Create a new data array
phwhall.0710.ordered <- phwhall_occobject_0710_2
# Change the data to the new ordered data
phwhall.0710.ordered$y <- y.0710.new
str(phwhall.0710.ordered)
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_dayyear.det.formula <- ~scale(dayyear)
# Number of species
N <- dim(phwhall.0710.ordered$y)[1]
# Distances between sites
dist.phwh <- dist(phwhall.0710.ordered$coords)
# Exponential covariance model
cov.model <- "exponential"
ms.inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(phwhall.0710.ordered$y, c(1, 2), max, na.rm = TRUE))
ms.priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
out.alltax.allsigcor.impdet.ms.0710 <- msPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula,
det.formula = phwhall_occobject_dayyear.det.formula,
data = phwhall.0710.ordered,
inits = ms.inits,
n.samples = 30000,
priors = ms.priors,
n.omp.threads = 4,
verbose = TRUE,
n.report = 10000,
n.burn = 5000,
n.thin = 50,
n.chains = 3)
summary(out.alltax.allsigcor.impdet.ms.0710, level = 'both')
ppc.alltax.allsigcor.impdet.ms.0710.out <- ppcOcc(out.alltax.allsigcor.impdet.ms.0710, 'chi-squared', group = 1)
summary(ppc.alltax.allsigcor.impdet.ms.0710.out, level = 'both')
waicOcc(out.alltax.allsigcor.impdet.ms.0710)
write_rds(out.alltax.allsigcor.impdet.ms.0710,"alltaxmodel_phwh_0710.rds")prediction across census streams - taxa richness?
rich.pred<-apply(out.alltax.allsigcor.impdet.ms.0710$z.samples, c(1,3), sum)
plot.dat.rich <- data.frame(x = pcap_full_land$xcoord,
y = pcap_full_land$ycoord,
mean.rich = apply(rich.pred, 2, mean),
sd.rich = apply(rich.pred, 2, sd),
mean.rich.R = apply(rich.pred.R,2,mean),
sd.rich.R = apply(rich.pred.R,2,sd),
mean.rich.S = apply(rich.pred.S,2,mean),
sd.rich.S = apply(rich.pred.S,2,sd),
mean.rich.T = apply(rich.pred.T,2,mean),
sd.rich.T = apply(rich.pred.T,2,sd),
stringsAsFactors = FALSE)
write.csv(plot.dat.rich,"plotdatarichness.csv")
dsp <- SpatialPoints(plot.dat.rich[,1:2])
dsp <- SpatialPointsDataFrame(dsp, plot.dat.rich)
crs(dsp)<-"+proj=lcc +lat_0=39.6666666666667 +lon_0=-82.5 +lat_1=41.7 +lat_2=40.4333333333333 +x_0=600000 +y_0=0 +datum=NAD83 +units=us-ft +no_defs"
j <- sample(nrow(dsp), 0.2 * nrow(dsp))
tst <- dsp[j,]
trn <- dsp[-j,]
RMSE <- function(observed, predicted) {
sqrt(mean((predicted - observed)^2, na.rm=TRUE))
}
f1 <- function(x, test, train) {
nmx <- x[1]
idp <- x[2]
if (nmx < 1) return(Inf)
if (idp < .001) return(Inf)
m <- gstat(formula=mean.rich.S~1, locations=train, nmax=nmx, set=list(idp=idp))
p <- predict(m, newdata=test, debug.level=0)$var1.pred
RMSE(test$mean.rich.S, p)
}
p <- data.frame(geom(dsp)[, c("x", "y")], as.data.frame(dsp))
r <- raster(respoly, res=100)
opt <- optim(c(8, 0.5), f1, test=tst, train=trn)
m <- gstat(formula=mean.rich.S~1, locations=~x+y, data=p, nmax=opt$par[1], set=list(idp=opt$par[2]))
idw <- interpolate(r, m, debug.level=0)
idw <- mask(idw, respoly)
plot(idw, 1)occupancy predictions
respoly<-readOGR("G:/NaturalResources/Byer/DataAnalysis/bbs_cmp_analyses/bbsapp/CMPboundaries.shp")
RMSE <- function(observed, predicted) {
sqrt(mean((predicted - observed)^2, na.rm=TRUE))
}
f1 <- function(x, test, train) {
nmx <- x[1]
idp <- x[2]
if (nmx < 1) return(Inf)
if (idp < .001) return(Inf)
m <- gstat(formula=mean.psi~1, locations=train, nmax=nmx, set=list(idp=idp))
p <- predict(m, newdata=test, debug.level=0)$var1.pred
RMSE(test$mean.psi, p)
}
idwrasters_occupancy_census<-list()
sppnames<-rownames(phwhall.0710.ordered$y)
saveRDS(sppnames,"sppnames_census.RData")
for(i in 1:length(sppnames)){
plot.dat.ms <- data.frame(x = phwhall.0710.ordered$coords[,1],
y = phwhall.0710.ordered$coords[,2],
mean.psi = apply(out.alltax.allsigcor.impdet.ms.0710$psi.samples[,i,], 2, mean))
dsp <- SpatialPoints(plot.dat.ms[,1:2])
dsp <- SpatialPointsDataFrame(dsp, plot.dat.ms)
crs(dsp)<-CRS("+init=epsg:3734")
j <- sample(nrow(dsp), 0.2 * nrow(dsp))
tst <- dsp[j,]
trn <- dsp[-j,]
opt <- optim(c(8, .5), f1, test=tst, train=trn)
# define groups for mapping
cuts <- c(0,0.25,0.75,1.0)
# set up a palette of interpolated colors
blues <- colorRampPalette(c('yellow', 'orange', 'blue', 'dark blue'))
spplot(dsp, 'mean.psi', cuts=cuts, col.regions=blues(5), pch=20, cex=2)
v <- voronoi(dsp)
ch <- chull(plot.dat.ms[,1:2])
coords <- plot.dat.ms[c(ch, ch[1]), 1:2] # closed polygon
plot(plot.dat.ms[,1:2], pch=19)
lines(coords, col="red")
r <- raster(respoly, res=500)
fitmax <- gstat::gstat(formula = mean.psi ~ 1, data = dsp, nmax = opt$par[1], set = list(idp = opt$par[2]))
maxint <- raster::interpolate(r, model=fitmax)
crs(maxint)<-"+proj=lcc +lat_0=39.6666666666667 +lon_0=-82.5 +lat_1=41.7 +lat_2=40.4333333333333 +x_0=600000 +y_0=0 +datum=NAD83 +units=us-ft +no_defs"
plot(maxint, col=rev(heat.colors(255)))
plot(vr, add=TRUE)
r2 <- crop(maxint, extent(respoly))
r3 <- mask(r2, respoly)
plot(r3)
idwrasters_occupancy_census[[i]]<-r3
writeRaster(r3,filename = paste0("G:/NaturalResources/Byer/DataAnalysis/PHWH/rasterpredictions/02082023_predictions/census/",sppnames[i],"_census_meanpsi_02082023.asc"),overwrite=TRUE)
}
saveRDS(idwrasters_occupancy_census,"idwrasters_occupancy_census.rData")
options(max.print=1000000)
sink("out.alltax.allsigcor.impdet.ms.0710.txt")
print(summary(out.alltax.allsigcor.impdet.ms.0710,level="species",quantiles=c(0.05,0.5,0.95)))
sink() occupancy model for 1316 alone
phwhall_occobject_1316_2<-phwhall_occobject_1316
phwhall_occobject_1316_2$y<-phwhall_occobject_1316$y[(apply(phwhall_occobject_1316$y, 1, mean, na.rm = TRUE) > 0),,]
dim(phwhall_occobject_1316_2$y)
sppnames_1316_2<-rownames(phwhall_occobject_1316_2$y)
sort(apply(phwhall_occobject_1316_2$y, 1, mean, na.rm = TRUE))
# louvain's cluster identified 3 clusters - one of (perhaps) more rocky stream communities, one with most fish that are indicators of # stream health, and another of more spring-associated communities.
#Place a common species first. The first species has all of its factor loadings set to fixed values, and so it can have a large #influence on the resulting interpretation on the factor loadings and latent factors. We have also found that having a very rare #species first can result in very slow mixing and increased sensitivity to initial values of the latent factor loadings matrix.
#For the remaining q−1 factors, place species that you believe will show different occurrence patterns than the first species, and #the other species placed before it. Place these remaining q−1 species in order of decreasing differences from the initial factor.
#For example, if I had q=3, for the second species in the array, I would place a species that I a priori think is most different from #the first species. For the third species in the array, I would place a species that I think will show different occurrence patterns #than both the first and second species, but its patterns may not be as noticeably different compared to the first and second species.
# Reorder species.
sp.1316.ordered <- c("other_flies","central_stoneroller_minnow","johnny_darter",sppnames_1316_2[!(sppnames_1316_2 %in% c("other_flies","central_stoneroller_minnow","johnny_darter"))])
# Create new detection-nondetection data matrix in the new order
y.1316.new <- phwhall_occobject_1316_2$y[sp.1316.ordered, , ]
# Create a new data array
phwhall.1316.ordered <- phwhall_occobject_1316_2
# Change the data to the new ordered data
phwhall.1316.ordered$y <- y.1316.new
str(phwhall.1316.ordered)
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_dayyear.det.formula <- ~scale(dayyear)
# Number of species
N <- dim(phwhall.1316.ordered$y)[1]
# Distances between sites
dist.phwh <- dist(phwhall.1316.ordered$coords)
# Exponential covariance model
cov.model <- "exponential"
ms.inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(phwhall.1316.ordered$y, c(1, 2), max, na.rm = TRUE))
ms.priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
out.alltax.allsigcor.impdet.ms.1316 <- msPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula,
det.formula = phwhall_occobject_dayyear.det.formula,
data = phwhall.1316.ordered,
inits = ms.inits,
n.samples = 30000,
priors = ms.priors,
n.omp.threads = 4,
verbose = TRUE,
n.report = 10000,
n.burn = 5000,
n.thin = 50,
n.chains = 3)
summary(out.alltax.allsigcor.impdet.ms.1316, level = 'both')
ppc.alltax.allsigcor.impdet.ms.1316.out <- ppcOcc(out.alltax.allsigcor.impdet.ms.1316, 'chi-squared', group = 1)
summary(ppc.alltax.allsigcor.impdet.ms.1316.out, level = 'both')
waicOcc(out.alltax.allsigcor.impdet.ms.1316)
write_rds(out.alltax.allsigcor.impdet.ms.1316,"alltaxmodel_phwh_1316.rds")occupancy predictions
respoly<-readOGR("G:/NaturalResources/Byer/DataAnalysis/bbs_cmp_analyses/bbsapp/CMPboundaries.shp")
RMSE <- function(observed, predicted) {
sqrt(mean((predicted - observed)^2, na.rm=TRUE))
}
f1 <- function(x, test, train) {
nmx <- x[1]
idp <- x[2]
if (nmx < 1) return(Inf)
if (idp < .001) return(Inf)
m <- gstat(formula=mean.psi~1, locations=train, nmax=nmx, set=list(idp=idp))
p <- predict(m, newdata=test, debug.level=0)$var1.pred
RMSE(test$mean.psi, p)
}
idwrasters_occupancy_first<-list()
sppnames<-rownames(phwhall.1316.ordered$y)
saveRDS(sppnames,"sppnames_first.RData")
for(i in 1:length(sppnames)){
plot.dat.ms <- data.frame(x = phwhall.1316.ordered$coords[,1],
y = phwhall.1316.ordered$coords[,2],
mean.psi = apply(out.alltax.allsigcor.impdet.ms.1316$psi.samples[,i,], 2, mean))
dsp <- SpatialPoints(plot.dat.ms[,1:2])
dsp <- SpatialPointsDataFrame(dsp, plot.dat.ms)
crs(dsp)<-CRS("+init=epsg:3734")
j <- sample(nrow(dsp), 0.2 * nrow(dsp))
tst <- dsp[j,]
trn <- dsp[-j,]
opt <- optim(c(8, .5), f1, test=tst, train=trn)
# define groups for mapping
cuts <- c(0,0.25,0.75,1.0)
# set up a palette of interpolated colors
blues <- colorRampPalette(c('yellow', 'orange', 'blue', 'dark blue'))
spplot(dsp, 'mean.psi', cuts=cuts, col.regions=blues(5), pch=20, cex=2)
v <- voronoi(dsp)
ch <- chull(plot.dat.ms[,1:2])
coords <- plot.dat.ms[c(ch, ch[1]), 1:2] # closed polygon
plot(plot.dat.ms[,1:2], pch=19)
lines(coords, col="red")
r <- raster(respoly, res=500)
fitmax <- gstat::gstat(formula = mean.psi ~ 1, data = dsp, nmax = opt$par[1], set = list(idp = opt$par[2]))
maxint <- raster::interpolate(r, model=fitmax)
crs(maxint)<-"+proj=lcc +lat_0=39.6666666666667 +lon_0=-82.5 +lat_1=41.7 +lat_2=40.4333333333333 +x_0=600000 +y_0=0 +datum=NAD83 +units=us-ft +no_defs"
plot(maxint, col=rev(heat.colors(255)))
plot(vr, add=TRUE)
r2 <- crop(maxint, extent(respoly))
r3 <- mask(r2, respoly)
plot(r3)
idwrasters_occupancy_first[[i]]<-r3
writeRaster(r3,filename = paste0("G:/NaturalResources/Byer/DataAnalysis/PHWH/rasterpredictions/02082023_predictions/first/",sppnames[i],"_first_meanpsi_02082023.asc"),overwrite=TRUE)
}
saveRDS(idwrasters_occupancy_first,"idwrasters_occupancy_first.RData")
options(max.print=1000000)
sink("out.alltax.allsigcor.impdet.ms.1316.txt")
print(summary(out.alltax.allsigcor.impdet.ms.1316,level="species",quantiles=c(0.05,0.5,0.95)))
sink() occupancy model for 1821 alone
phwhall_occobject_1821_2<-phwhall_occobject_1821
phwhall_occobject_1821_2$y<-phwhall_occobject_1821$y[(apply(phwhall_occobject_1821$y, 1, mean, na.rm = TRUE) > 0),,]
dim(phwhall_occobject_1821_2$y)
sppnames_1821_2<-rownames(phwhall_occobject_1821_2$y)
sort(apply(phwhall_occobject_1821_2$y, 1, mean, na.rm = TRUE))
# louvain's cluster identified 3 clusters - one of (perhaps) more rocky stream communities, one with most fish that are indicators of # stream health, and another of more spring-associated communities.
#Place a common species first. The first species has all of its factor loadings set to fixed values, and so it can have a large #influence on the resulting interpretation on the factor loadings and latent factors. We have also found that having a very rare #species first can result in very slow mixing and increased sensitivity to initial values of the latent factor loadings matrix.
#For the remaining q−1 factors, place species that you believe will show different occurrence patterns than the first species, and #the other species placed before it. Place these remaining q−1 species in order of decreasing differences from the initial factor.
#For example, if I had q=3, for the second species in the array, I would place a species that I a priori think is most different from #the first species. For the third species in the array, I would place a species that I think will show different occurrence patterns #than both the first and second species, but its patterns may not be as noticeably different compared to the first and second species.
# Reorder species.
sp.1821.ordered <- c("midges","streambiotic_suff_mole",sppnames_1821_2[!(sppnames_1821_2 %in% c("midges","streambiotic_suff_mole"))])
# Create new detection-nondetection data matrix in the new order
y.1821.new <- phwhall_occobject_1821_2$y[sp.1821.ordered, , ]
# Create a new data array
phwhall.1821.ordered <- phwhall_occobject_1821_2
# Change the data to the new ordered data
phwhall.1821.ordered$y <- y.1821.new
str(phwhall.1821.ordered)
phwhall_occobject_allsigcor.occ.formula <- ~scale(pool_depth_.cm.) + scale(per_sand) + scale(per_cobble) + scale(substrate_metric_total) +
scale(pool_metric_total)
phwhall_occobject_dayyear.det.formula <- ~scale(dayyear)
# Number of species
N <- dim(phwhall.1821.ordered$y)[1]
# Distances between sites
dist.phwh <- dist(phwhall.1821.ordered$coords)
# Exponential covariance model
cov.model <- "exponential"
ms.inits <- list(alpha.comm = 0,
beta.comm = 0,
beta = 0,
alpha = 0,
tau.sq.beta = 1,
tau.sq.alpha = 1,
z = apply(phwhall.1821.ordered$y, c(1, 2), max, na.rm = TRUE))
ms.priors <- list(beta.comm.normal = list(mean = 0, var = 2.72),
alpha.comm.normal = list(mean = 0, var = 2.72),
tau.sq.beta.ig = list(a = 0.1, b = 0.1),
tau.sq.alpha.ig = list(a = 0.1, b = 0.1))
out.alltax.allsigcor.impdet.ms.1821 <- msPGOcc(occ.formula = phwhall_occobject_allsigcor.occ.formula,
det.formula = phwhall_occobject_dayyear.det.formula,
data = phwhall.1821.ordered,
inits = ms.inits,
n.samples = 30000,
priors = ms.priors,
n.omp.threads = 4,
verbose = TRUE,
n.report = 10000,
n.burn = 5000,
n.thin = 50,
n.chains = 3)
summary(out.alltax.allsigcor.impdet.ms.1821, level = 'both')
ppc.alltax.allsigcor.impdet.ms.1821.out <- ppcOcc(out.alltax.allsigcor.impdet.ms.1821, 'chi-squared', group = 1)
summary(ppc.alltax.allsigcor.impdet.ms.1821.out, level = 'both')
waicOcc(out.alltax.allsigcor.impdet.ms.1821)
write_rds(out.alltax.allsigcor.impdet.ms.1821,"alltaxmodel_phwh_1821.rds")occupancy predictions
respoly<-readOGR("G:/NaturalResources/Byer/DataAnalysis/bbs_cmp_analyses/bbsapp/CMPboundaries.shp")
RMSE <- function(observed, predicted) {
sqrt(mean((predicted - observed)^2, na.rm=TRUE))
}
f1 <- function(x, test, train) {
nmx <- x[1]
idp <- x[2]
if (nmx < 1) return(Inf)
if (idp < .001) return(Inf)
m <- gstat(formula=mean.psi~1, locations=train, nmax=nmx, set=list(idp=idp))
p <- predict(m, newdata=test, debug.level=0)$var1.pred
RMSE(test$mean.psi, p)
}
idwrasters_occupancy_second<-list()
sppnames<-rownames(phwhall.1821.ordered$y)
saveRDS(sppnames,"sppnames_second.RData")
for(i in 1:length(sppnames)){
plot.dat.ms <- data.frame(x = phwhall.1821.ordered$coords[,1],
y = phwhall.1821.ordered$coords[,2],
mean.psi = apply(out.alltax.allsigcor.impdet.ms.1821$psi.samples[,i,], 2, mean))
dsp <- SpatialPoints(plot.dat.ms[,1:2])
dsp <- SpatialPointsDataFrame(dsp, plot.dat.ms)
crs(dsp)<-CRS("+init=epsg:3734")
j <- sample(nrow(dsp), 0.2 * nrow(dsp))
tst <- dsp[j,]
trn <- dsp[-j,]
opt <- optim(c(8, .5), f1, test=tst, train=trn)
# define groups for mapping
cuts <- c(0,0.25,0.75,1.0)
# set up a palette of interpolated colors
blues <- colorRampPalette(c('yellow', 'orange', 'blue', 'dark blue'))
spplot(dsp, 'mean.psi', cuts=cuts, col.regions=blues(5), pch=20, cex=2)
v <- voronoi(dsp)
ch <- chull(plot.dat.ms[,1:2])
coords <- plot.dat.ms[c(ch, ch[1]), 1:2] # closed polygon
plot(plot.dat.ms[,1:2], pch=19)
lines(coords, col="red")
r <- raster(respoly, res=500)
fitmax <- gstat::gstat(formula = mean.psi ~ 1, data = dsp, nmax = opt$par[1], set = list(idp = opt$par[2]))
maxint <- raster::interpolate(r, model=fitmax)
crs(maxint)<-"+proj=lcc +lat_0=39.6666666666667 +lon_0=-82.5 +lat_1=41.7 +lat_2=40.4333333333333 +x_0=600000 +y_0=0 +datum=NAD83 +units=us-ft +no_defs"
plot(maxint, col=rev(heat.colors(255)))
plot(vr, add=TRUE)
r2 <- crop(maxint, extent(respoly))
r3 <- mask(r2, respoly)
plot(r3)
idwrasters_occupancy_second[[i]]<-r3
writeRaster(r3,filename = paste0("G:/NaturalResources/Byer/DataAnalysis/PHWH/rasterpredictions/02082023_predictions/second/",sppnames[i],"_second_meanpsi_02082023.asc"),overwrite=TRUE)
}
saveRDS(idwrasters_occupancy_second,"idwrasters_occupancy_second.RData")
options(max.print=1000000)
sink("out.alltax.allsigcor.impdet.ms.1821.txt")
print(summary(out.alltax.allsigcor.impdet.ms.1821,level="species",quantiles=c(0.05,0.5,0.95)))
sink()